train.py

import logging
import math
import os
import cv2
import random
import shutil
from pathlib import Path
import pickle

import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as tf

import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from torchvision import utils

import diffusers
from diffusers import (
    AutoencoderKL,
    # ControlNetModel,
    DDPMScheduler,
    # StableDiffusionControlNetPipeline,
    UNet2DConditionModel,
    UniPCMultistepScheduler,
)

from models.controlnet1x1 import ControlNetModel1x1 as ControlNetModel
from models.pipeline_controlnet_1x1_4dunet import StableDiffusionControlNetPipeline1x1 as StableDiffusionControlNetPipeline

from models.unet_2d_condition_multiview import UNet2DConditionModelMultiview

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 diffusers.utils.torch_utils import is_compiled_module

from utils.common import convert_outputs_to_fp16, unet_param
from utils.dataset_nusmtv import NuScenesDatasetMtvSpar as NuScenesDataset




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("0.26.0.dev0")

logger = get_logger(__name__)



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

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

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


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

    controlnet = accelerator.unwrap_model(controlnet)

    pipeline = StableDiffusionControlNetPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        controlnet=controlnet,
        safety_checker=None,
        revision=args.revision,
        variant=args.variant,
        torch_dtype=weight_dtype,
    )
    pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    if args.enable_xformers_memory_efficient_attention:
        pipeline.enable_xformers_memory_efficient_attention()

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


    for img_idx in [0, 678, 1879, ]:
        data_dict = val_dataset.__getitem__(img_idx)
        mtv_condition = data_dict['ctrl_img']

        validation_prompts = ['show a photorealistic street view image'] * 6
        images_tensor = []

        for _ in range(args.num_validation_images):
            with torch.autocast("cuda"):
                image = pipeline(
                    prompt=validation_prompts, image=mtv_condition, num_inference_steps=20, generator=generator, height=args.height, width=args.width, controlnet_conditioning_scale=1.0, guidance_scale=args.cfg_scale
                ).images#[0]
            image = torch.cat([torch.tensor(np.array(ii)) for ii in image], 1)

            images_tensor.append(image)

        # [448, 6, 800, 3] to [448, 4800, 3]
        raw_img = data_dict['pixel_values'].permute(2,0,3,1).reshape(images_tensor[0].shape) * 255
        # print(data_dict['occ_rgb'].shape)
        occ_rgb = data_dict['occ_rgb'].permute(1,0,2,3).reshape(images_tensor[0].shape)
        gen_img = torch.cat(images_tensor, 0)
        gen_img = torch.cat([occ_rgb, gen_img, raw_img], 0)

        out_path = os.path.join(
            args.output_dir,
            f"step_{step:06d}_{img_idx:04d}.jpg",
        )

        cv2.imwrite(out_path, cv2.cvtColor(gen_img.cpu().numpy(), cv2.COLOR_RGB2BGR))

    return None



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

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

        return CLIPTextModel
    elif model_class == "RobertaSeriesModelWithTransformation":
        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation

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


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

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
    """
    model_card = f"""
# controlnet-{repo_id}

These are controlnet weights trained on {base_model} with new type of conditioning.
{img_str}
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def main(args):
    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,
    )

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

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

    # Load scheduler and models
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    text_encoder = text_encoder_cls.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
    )
    vae = AutoencoderKL.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
    )
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
    )

    if args.controlnet_model_name_or_path:
        logger.info("Loading existing controlnet weights")
        controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
    else:
        logger.info("Initializing controlnet weights from unet")
        controlnet = ControlNetModel.from_unet(unet, conditioning_channels=args.ctrl_channel)
        # controlnet = ControlNetModel.from_unet(unet)

    unet = UNet2DConditionModelMultiview.from_unet_2d_condition(unet, **unet_param)

    # Resuming unet
    if args.resume_from_checkpoint == "latest":
        # 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 unet from checkpoint {path}")
            unet = unet.from_pretrained(os.path.join(args.output_dir, path), subfolder="unet")

    # 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:
                i = len(weights) - 1

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

                    sub_dir = "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 = ControlNetModel.from_pretrained(input_dir, subfolder="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)

    vae.requires_grad_(False)
    unet.requires_grad_(False)
    text_encoder.requires_grad_(False)
    controlnet.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.warn(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            unet.enable_xformers_memory_efficient_attention()
            controlnet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    if args.gradient_checkpointing:
        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(controlnet).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {unwrap_model(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 = list(controlnet.parameters())
    unet.to(accelerator.device, dtype=torch.float16)

    for name, mod in unet.trainable_module.items():
        mod.requires_grad_(True)

    for name, mod in unet.trainable_module.items():
        logging.debug(f"[MultiviewRunner] set {name} to fp32")
        mod.to(dtype=torch.float32)
        mod._original_forward = mod.forward
        # autocast intermediate is necessary since others are fp16
        mod.forward = torch.cuda.amp.autocast(
            dtype=torch.float16)(mod.forward)
        # we ensure output is always fp16
        mod.forward = convert_outputs_to_fp16(mod.forward)

    unet_params = unet.trainable_parameters
    params_to_optimize += unet_params


    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,
    )

    # train_dataset = NuScenesDatasetMtvCBGS(args, tokenizer, 'train')
    train_dataset = NuScenesDataset(args, tokenizer, 'train')
    val_dataset = NuScenesDataset(args, tokenizer, 'val')

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

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

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

    # Prepare everything with our `accelerator`.
    controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        controlnet, optimizer, train_dataloader, 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, unet and text_encoder to device and cast to weight_dtype
    vae.to(accelerator.device, dtype=weight_dtype)
    # unet.to(accelerator.device, dtype=weight_dtype)
    text_encoder.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))

        # 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}")
            # import ipdb; ipdb.set_trace()
            accelerator.load_state(os.path.join(args.output_dir, path))
            # unet = unet.from_pretrained(os.path.join(args.output_dir, path), subfolder="unet")

            global_step = int(path.split("-")[1])

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

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

    image_logs = None
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(controlnet):

                batch["pixel_values"] = batch["pixel_values"][0]
                batch["input_ids"] = batch["input_ids"][0]
                ctrl_img = batch['ctrl_img'][0]
                weight_mask = batch['weight_mask'][0]

                batch["conditioning_pixel_values"] = ctrl_img

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

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

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

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

                controlnet_image = batch["conditioning_pixel_values"].to(device=latents.device, dtype=weight_dtype)

                down_block_res_samples, mid_block_res_sample = controlnet(
                    noisy_latents,
                    timesteps,
                    encoder_hidden_states=encoder_hidden_states,
                    controlnet_cond=controlnet_image,
                    return_dict=False,
                )

                model_pred = unet(
                    noisy_latents,
                    timesteps,
                    encoder_hidden_states=encoder_hidden_states,
                    down_block_additional_residuals=[
                        sample.to(dtype=weight_dtype) for sample in down_block_res_samples
                    ],
                    mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
                    return_dict=False,
                )[0]

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

                def weighted_mse_loss(input, target, weight):
                    return torch.mean(weight * (input - target) ** 2)

                weight_mask = weight_mask.to(dtype=weight_dtype)
                weight_mask, depth_map = weight_mask[:, 0, ...], weight_mask[:, 1, ...]

                depth_map = -0.5 * (1 + torch.cos(torch.pi * depth_map / 255)) + 2.1
                fore = -0.5 * (1 + np.cos(np.pi * global_step / args.max_train_steps)) + 2.2

                curr_mask = weight_mask==2

                weight_mask[curr_mask] = depth_map[curr_mask] * fore

                loss = weighted_mse_loss(model_pred.float(), target.float(), weight_mask.unsqueeze(1).repeat(1,target.shape[1],1,1))

                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = 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

                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)

                        # save unet
                        accelerator.unwrap_model(unet).save_pretrained(os.path.join(save_path, 'unet'))

                        logger.info(f"Saved state to {save_path}")

                    if global_step % args.validation_steps == 0:
                        image_logs = log_validation(
                            vae,
                            text_encoder,
                            tokenizer,
                            unet,
                            controlnet,
                            args,
                            accelerator,
                            weight_dtype,
                            global_step,
                            val_dataset
                        )

            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:
        controlnet = unwrap_model(controlnet)
        controlnet.save_pretrained(os.path.join(args.output_dir, f"checkpoint-{global_step}", 'controlnet'))
        unwrap_model(unet).save_pretrained(os.path.join(args.output_dir, f"checkpoint-{global_step}", 'unet'))

        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__":
    from args_file import parse_args
    args = parse_args()
    main(args)