train_text_to_image_control_lora.py

"""Fine-tuning script for Stable Diffusion for text2image with support for ControlLoRA."""
"""Code refer to https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py"""

from diffusers import utils
from diffusers.utils import deprecation_utils
from diffusers.models import cross_attention
utils.deprecate = lambda *arg, **kwargs: None
deprecation_utils.deprecate = lambda *arg, **kwargs: None
cross_attention.deprecate = lambda *arg, **kwargs: None

import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Optional

import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfFolder, Repository, create_repo, whoami
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL, 
    DDPMScheduler, 
    DPMSolverMultistepScheduler, 
    DiffusionPipeline, 
    UNet2DConditionModel)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from models import ControlLoRA
from process import DatasetBase as dataset_cls


# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.13.0.dev0")

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


def save_model_card(repo_name, images=None, base_model=str, dataset_name=str, repo_folder=None):
    img_str = ""
    for i, image in enumerate(images):
        image.save(os.path.join(repo_folder, f"image_{i}.png"))
        img_str += f"![img_{i}](./image_{i}.png)\n"

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- controlnet
- control-lora
inference: true
---
    """
    model_card = f"""
# ControlLoRA text2image fine-tuning - {repo_name}
These are ControlLoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
{img_str}
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default=None,
        required=True,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
            " or to a folder containing files that 🤗 Datasets can understand."
        ),
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The config of the Dataset, leave as None if there's only one config.",
    )
    parser.add_argument(
        "--train_data_dir",
        type=str,
        default=None,
        help=(
            "A folder containing the training data. Folder contents must follow the structure described in"
            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
        ),
    )
    parser.add_argument(
        "--image_column", type=str, default="image", help="The column of the dataset containing an image."
    )
    parser.add_argument(
        "--guide_column", type=str, default="guide", help="The column of the dataset containing a guide image."
    )
    parser.add_argument(
        "--caption_column",
        type=str,
        default="text",
        help="The column of the dataset containing a caption or a list of captions.",
    )
    parser.add_argument(
        "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=4,
        help="Number of images that should be generated during validation with `validation_prompt`.",
    )
    parser.add_argument(
        "--validation_epochs",
        type=int,
        default=1,
        help=(
            "Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`."
        ),
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="sd-fill50k-model-control-lora",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="The directory where the downloaded models and datasets will be stored.",
    )
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--resolution",
        type=int,
        default=512,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=100)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--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(
        "--mixed_precision",
        type=str,
        default=None,
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
            " training using `--resume_from_checkpoint`."
        ),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument("--control_lora_config", type=str, required=True, help="Config file of ControlLora")

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

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

    return args


def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
    if token is None:
        token = HfFolder.get_token()
    if organization is None:
        username = whoami(token)["name"]
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"


DATASET_NAME_MAPPING = {
    "HighCWu/fill50k": ("image", "guide", "text"),
    "HighCWu/diffusiondb_2m_first_5k_canny": ("image", "guide", "text")
}


def main():
    args = parse_args()
    logging_dir = os.path.join(args.output_dir, args.logging_dir)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=args.report_to,
        logging_dir=logging_dir,
    )
    if args.report_to == "wandb":
        if not is_wandb_available():
            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
        import wandb

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

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

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo_name = create_repo(repo_name, exist_ok=True)
            repo = Repository(args.output_dir, clone_from=repo_name)

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

    # Load scheduler, tokenizer and models.
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    tokenizer = CLIPTokenizer.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
    )
    text_encoder = CLIPTextModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
    )
    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
    )
    
    n_ch = len(unet.config.block_out_channels)
    control_ids = [i for i in range(n_ch)]
    cross_attention_dims = {i: [] for i in range(n_ch)}
    for name in unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            control_id = control_ids[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            control_id = list(reversed(control_ids))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            control_id = control_ids[block_id]
        cross_attention_dims[control_id].append(cross_attention_dim)
    cross_attention_dims = tuple([cross_attention_dims[control_id] for control_id in control_ids])

    control_lora = ControlLoRA.from_config(args.control_lora_config)

    # freeze parameters of models to save more memory
    unet.requires_grad_(False)
    vae.requires_grad_(False)

    text_encoder.requires_grad_(False)

    # For mixed precision training we cast 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 unet, vae and text_encoder to device and cast to weight_dtype
    unet.to(accelerator.device, dtype=weight_dtype)
    vae.to(accelerator.device, dtype=weight_dtype)
    text_encoder.to(accelerator.device, dtype=weight_dtype)
    control_lora.to(accelerator.device) # control_lora.to(accelerator.device), dtype=weight_dtype)

    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    # now we will add new LoRA weights to the attention layers
    # It's important to realize here how many attention weights will be added and of which sizes
    # The sizes of the attention layers consist only of two different variables:
    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.

    # Let's first see how many attention processors we will have to set.
    # For Stable Diffusion, it should be equal to:
    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
    # => 32 layers

    # Set correct lora layers
    lora_attn_procs = {}
    lora_layers_list = list([list(layer_list) for layer_list in control_lora.lora_layers])
    for name in unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            control_id = control_ids[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            control_id = list(reversed(control_ids))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            control_id = control_ids[block_id]

        lora_layers = lora_layers_list[control_id]
        if len(lora_layers) != 0:
            lora_layer = lora_layers.pop(0)
            lora_attn_procs[name] = lora_layer

    unet.set_attn_processor(lora_attn_procs)

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

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

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

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

    optimizer = optimizer_cls(
        control_lora.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
    
    # Preprocessing the datasets.
    # We need to tokenize input captions and transform the images.
    def tokenize_captions(examples, is_train=True):
        captions = []
        for caption in examples[caption_column]:
            if isinstance(caption, str):
                captions.append(caption)
            elif isinstance(caption, (list, np.ndarray)):
                # take a random caption if there are multiple
                captions.append(random.choice(caption) if is_train else caption[0])
            else:
                raise ValueError(
                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
                )
        inputs = tokenizer(
            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
        )
        return inputs.input_ids

    # Get the 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.
    global dataset_cls
    use_custom_dataset = False
    if args.dataset_name.startswith('process/'):
        # Use custom dataset define in process
        use_custom_dataset = True
        dataset_cls = dataset_cls.from_name(args.dataset_name)
        dataset = dataset_cls(tokenize_captions, resolution=args.resolution, use_crop=True)
    elif args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        dataset = load_dataset(
            args.dataset_name,
            args.dataset_config_name,
            cache_dir=args.cache_dir,
        )
    else:
        data_files = {}
        if args.train_data_dir is not None:
            data_files["train"] = os.path.join(args.train_data_dir, "**")
        dataset = load_dataset(
            "imagefolder",
            data_files=data_files,
            cache_dir=args.cache_dir,
        )
        # See more about loading custom images at
        # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder

    if use_custom_dataset:
        collate_fn = None
        train_dataset = dataset
        caption_column = args.caption_column
    else:
        # Preprocessing the datasets.
        # We need to tokenize inputs and targets.
        column_names = dataset["train"].column_names

        # 6. Get the column names for input/target.
        dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
        if args.image_column is None:
            image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
        else:
            image_column = args.image_column
            if image_column not in column_names:
                raise ValueError(
                    f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
                )
        if args.guide_column is None:
            guide_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
        else:
            guide_column = args.guide_column
            if guide_column not in column_names:
                raise ValueError(
                    f"--guide_column' value '{args.guide_column}' needs to be one of: {', '.join(column_names)}"
                )
        if args.caption_column is None:
            caption_column = dataset_columns[2] if dataset_columns is not None else column_names[2]
        else:
            caption_column = args.caption_column
            if caption_column not in column_names:
                raise ValueError(
                    f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
                )

        # Preprocessing the datasets.
        train_transforms = transforms.Compose(
            [
                transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        def preprocess_train(examples):
            images, guides = [], []
            for image, guide in zip(examples[image_column], examples[guide_column]):
                image, guide = image.convert("RGB"), guide.convert("RGB")
                image, guide = train_transforms(image), train_transforms(guide)
                c, h, w = image.shape
                y1, x1 = 0, 0
                if h != args.resolution:
                    y1 = torch.randint(0, h - args.resolution, (1, )).item()
                elif w != args.resolution:
                    x1 = torch.randint(0, w - args.resolution, (1, )).item()
                y2, x2 = y1 + args.resolution, x1 + args.resolution
                image = image[:,y1:y2,x1:x2]
                guide = guide[:,y1:y2,x1:x2]
                images.append(image)
                guides.append(guide)
                
            examples["pixel_values"] = images
            examples["guide_values"] = guides
            examples["input_ids"] = tokenize_captions(examples)
            return examples

        with accelerator.main_process_first():
            if args.max_train_samples is not None:
                dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
            # Set the training transforms
            train_dataset = dataset["train"].with_transform(preprocess_train)

        def collate_fn(examples):
            pixel_values = torch.stack([example["pixel_values"] for example in examples])
            pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
            guide_values = torch.stack([example["guide_values"] for example in examples])
            guide_values = guide_values.to(memory_format=torch.contiguous_format).float()
            input_ids = torch.stack([example["input_ids"] for example in examples])
            return {"pixel_values": pixel_values, "guide_values": guide_values, "input_ids": input_ids}

    # DataLoaders creation:
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        shuffle=True,
        collate_fn=collate_fn,
        batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
    )
    val_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        shuffle=False,
        collate_fn=collate_fn,
        batch_size=1,
        num_workers=0,
    )
    val_iter = iter(val_dataloader)

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

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

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

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

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

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

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

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

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

            resume_global_step = global_step * args.gradient_accumulation_steps
            first_epoch = global_step // num_update_steps_per_epoch
            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)

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

    for epoch in range(first_epoch, args.num_train_epochs):
        unet.train()
        train_loss = 0.0
        for step, batch in enumerate(train_dataloader):
            # Skip steps until we reach the resumed step
            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
                if step % args.gradient_accumulation_steps == 0:
                    progress_bar.update(1)
                continue

            with accelerator.accumulate(unet):
                # Convert images to latent space
                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
                latents = latents * vae.config.scaling_factor

                # Sample noise that we'll add to the latents
                noise = torch.randn_like(latents)
                bsz = latents.shape[0]
                # Sample a random timestep for each image
                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
                timesteps = timesteps.long()

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

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

                # Inject control states to unet
                _ = control_lora(batch["guide_values"].to(dtype=weight_dtype)).control_states

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

                # Predict the noise residual and compute loss
                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

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

                # Backpropagate
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = control_lora.parameters()
                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1
                accelerator.log({"train_loss": train_loss}, step=global_step)
                train_loss = 0.0

                if global_step % args.checkpointing_steps == 0:
                    if accelerator.is_main_process:
                        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:
                            logger.info(
                                f"Running sampling... \n Generating {args.num_validation_images} images with prompt:"
                                f" {args.validation_prompt}."
                            )
                            # create pipeline
                            pipeline = DiffusionPipeline.from_pretrained(
                                args.pretrained_model_name_or_path,
                                unet=accelerator.unwrap_model(unet),
                                revision=args.revision,
                                torch_dtype=weight_dtype,
                                safety_checker=None
                            )
                            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
                            pipeline = pipeline.to(accelerator.device)
                            pipeline.set_progress_bar_config(disable=True)

                            # run inference
                            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
                            images = []
                            for _ in range(args.num_validation_images):
                                with torch.no_grad():
                                    try:
                                        batch = next(val_iter)
                                    except:
                                        val_iter = iter(val_dataloader)
                                        batch = next(val_iter)
                                    target = batch["pixel_values"].to(dtype=weight_dtype)
                                    guide = batch["guide_values"].to(accelerator.device)
                                    _ = control_lora(guide).control_states
                                    image = pipeline(
                                        args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
                                    image = dataset_cls.cat_input(image, target, guide)
                                images.append(image)

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

                            del pipeline
                            torch.cuda.empty_cache()

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

            if global_step >= args.max_train_steps:
                break
        
        if accelerator.is_main_process:
            if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
                logger.info(
                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
                    f" {args.validation_prompt}."
                )
                # create pipeline
                pipeline = DiffusionPipeline.from_pretrained(
                    args.pretrained_model_name_or_path,
                    unet=accelerator.unwrap_model(unet),
                    revision=args.revision,
                    torch_dtype=weight_dtype,
                    safety_checker=None
                )
                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
                pipeline = pipeline.to(accelerator.device)
                pipeline.set_progress_bar_config(disable=True)

                # run inference
                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
                images = []
                for _ in range(args.num_validation_images):
                    with torch.no_grad():
                        try:
                            batch = next(val_iter)
                        except:
                            val_iter = iter(val_dataloader)
                            batch = next(val_iter)
                        target = batch["pixel_values"].to(dtype=weight_dtype)
                        guide = batch["guide_values"].to(accelerator.device)
                        _ = control_lora(guide).control_states
                        image = pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
                        image = dataset_cls.cat_input(image, target, guide)
                    images.append(image)

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

                del pipeline
                torch.cuda.empty_cache()

    # Save the lora layers
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = unet.to(torch.float32)
        # unet.save_attn_procs(args.output_dir)
        control_lora.save_config(args.output_dir)
        control_lora.save_pretrained(args.output_dir, safe_serialization=False)
        control_lora.save_pretrained(args.output_dir, safe_serialization=True)

        if args.push_to_hub:
            save_model_card(
                repo_name,
                images=images,
                base_model=args.pretrained_model_name_or_path,
                dataset_name=args.dataset_name,
                repo_folder=args.output_dir,
            )
            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)

    # Final inference
    # Load previous pipeline
    pipeline = DiffusionPipeline.from_pretrained(
        args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype, safety_checker=None
    )
    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
    pipeline = pipeline.to(accelerator.device)

    # load attention processors
    lora_attn_procs = {}
    lora_layers_list = list([list(layer_list) for layer_list in control_lora.lora_layers])
    for name in pipeline.unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            control_id = control_ids[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            control_id = list(reversed(control_ids))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            control_id = control_ids[block_id]

        lora_layers = lora_layers_list[control_id]
        if len(lora_layers) != 0:
            lora_layer = lora_layers.pop(0)
            lora_attn_procs[name] = lora_layer

    pipeline.unet.set_attn_processor(lora_attn_procs)

    # run inference
    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
    images = []
    for _ in range(args.num_validation_images):
        with torch.no_grad():
            try:
                batch = next(val_iter)
            except:
                val_iter = iter(val_dataloader)
                batch = next(val_iter)
            target = batch["pixel_values"].to(dtype=weight_dtype)
            guide = batch["guide_values"].to(accelerator.device)
            _ = control_lora(guide).control_states
            image = pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
            image = dataset_cls.cat_input(image, target, guide)
        images.append(image)

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

    accelerator.end_training()


if __name__ == "__main__":
    main()