train_dreambooth.py

import argparse
import itertools
import math
import os
from pathlib import Path
from typing import Optional
import subprocess
import sys
import gc
import random

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset

from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer


logger = get_logger(__name__)


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(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--instance_data_dir",
        type=str,
        default=None,
        #required=True,
        help="A folder containing the training data of instance images.",
    )
    parser.add_argument(
        "--class_data_dir",
        type=str,
        default=None,
        #required=False,
        help="A folder containing the training data of class images.",
    )
    parser.add_argument(
        "--instance_prompt",
        type=str,
        default=None,
        help="The prompt with identifier specifying the instance",
    )
    parser.add_argument(
        "--class_prompt",
        type=str,
        default="",
        help="The prompt to specify images in the same class as provided instance images.",
    )
    parser.add_argument(
        "--with_prior_preservation",
        default=False,
        action="store_true",
        help="Flag to add prior preservation loss.",
    )
    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
    parser.add_argument(
        "--num_class_images",
        type=int,
        default=100,
        help=(
            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
            " sampled with class_prompt."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    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(
        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
    )
    parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
    parser.add_argument(
        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument(
        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
    )
    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(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-6,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    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="no",
        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."
        ),
    )

    parser.add_argument(
        "--save_n_steps",
        type=int,
        default=1,
        help=("Save the model every n global_steps"),
    )
    
    
    parser.add_argument(
        "--save_starting_step",
        type=int,
        default=1,
        help=("The step from which it starts saving intermediary checkpoints"),
    )
    
    parser.add_argument(
        "--stop_text_encoder_training",
        type=int,
        default=1000000,
        help=("The step at which the text_encoder is no longer trained"),
    )


    parser.add_argument(
        "--image_captions_filename",
        action="store_true",
        help="Get captions from filename",
    )    
    
    
    parser.add_argument(
        "--dump_only_text_encoder",
        action="store_true",
        default=False,        
        help="Dump only text encoder",
    )

    parser.add_argument(
        "--train_only_unet",
        action="store_true",
        default=False,        
        help="Train only the unet",
    )
    
    parser.add_argument(
        "--cache_latents",
        action="store_true",
        default=False,        
        help="Train only the unet",
    )
    
    parser.add_argument(
        "--Session_dir",
        type=str,
        default="",     
        help="Current session directory",
    )    

    
    

    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")

    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

    #if args.instance_data_dir is None:
    #    raise ValueError("You must specify a train data directory.")

    #if args.with_prior_preservation:
    #    if args.class_data_dir is None:
    #        raise ValueError("You must specify a data directory for class images.")
    #    if args.class_prompt is None:
    #        raise ValueError("You must specify prompt for class images.")

    return args


class DreamBoothDataset(Dataset):
    """
    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
    It pre-processes the images and the tokenizes prompts.
    """

    def __init__(
        self,
        instance_data_root,
        instance_prompt,
        tokenizer,
        args,
        class_data_root=None,
        class_prompt=None,
        size=512,
        center_crop=False,
    ):
        self.size = size
        self.center_crop = center_crop
        self.tokenizer = tokenizer
        self.image_captions_filename = None

        self.instance_data_root = Path(instance_data_root)
        if not self.instance_data_root.exists():
            raise ValueError("Instance images root doesn't exists.")

        self.instance_images_path = list(Path(instance_data_root).iterdir())
        self.num_instance_images = len(self.instance_images_path)
        self.instance_prompt = instance_prompt
        self._length = self.num_instance_images

        if args.image_captions_filename:
            self.image_captions_filename = True
        
        if class_data_root is not None:
            self.class_data_root = Path(class_data_root)
            self.class_data_root.mkdir(parents=True, exist_ok=True)
            self.class_images_path = list(self.class_data_root.iterdir())
            random.shuffle(self.class_images_path)
            self.num_class_images = len(self.class_images_path)
            self._length = max(self.num_class_images, self.num_instance_images)
            self.class_prompt = class_prompt
        else:
            self.class_data_root = None

        self.image_transforms = transforms.Compose(
            [
                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

    def __len__(self):
        return self._length

    def __getitem__(self, index):
        example = {}
        path = self.instance_images_path[index % self.num_instance_images]
        instance_image = Image.open(path)
        if not instance_image.mode == "RGB":
            instance_image = instance_image.convert("RGB")
            
        instance_prompt = self.instance_prompt
        
        if self.image_captions_filename:
            filename = Path(path).stem
            pt=''.join([i for i in filename if not i.isdigit()])
            pt=pt.replace("_"," ")
            pt=pt.replace("(","")
            pt=pt.replace(")","")
            pt=pt.replace("-","")
            instance_prompt = pt
            sys.stdout.write(" " +instance_prompt+" ")
            sys.stdout.flush()


        example["instance_images"] = self.image_transforms(instance_image)
        example["instance_prompt_ids"] = self.tokenizer(
            instance_prompt,
            padding="do_not_pad",
            truncation=True,
            max_length=self.tokenizer.model_max_length,
        ).input_ids

        if self.class_data_root:
            class_image = Image.open(self.class_images_path[index % self.num_class_images])
            if not class_image.mode == "RGB":
                class_image = class_image.convert("RGB")
            example["class_images"] = self.image_transforms(class_image)
            example["class_prompt_ids"] = self.tokenizer(
                self.class_prompt,
                padding="do_not_pad",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
            ).input_ids

        return example



class PromptDataset(Dataset):
    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."

    def __init__(self, prompt, num_samples):
        self.prompt = prompt
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, index):
        example = {}
        example["prompt"] = self.prompt
        example["index"] = index
        return example

class LatentsDataset(Dataset):
    def __init__(self, latents_cache, text_encoder_cache):
        self.latents_cache = latents_cache
        self.text_encoder_cache = text_encoder_cache

    def __len__(self):
        return len(self.latents_cache)

    def __getitem__(self, index):
        return self.latents_cache[index], self.text_encoder_cache[index]

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}"

def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
    """
    Starts from base starting dict and then adds the remaining key values from updater replacing the values from
    the first starting/base dict with the second updater dict.

    For later: how does d = {**d1, **d2} replace collision?

    :param starting_dict:
    :param updater_dict:
    :return:
    """
    new_dict: dict = starting_dict.copy()   # start with keys and values of starting_dict
    new_dict.update(updater_dict)    # modifies starting_dict with keys and values of updater_dict
    return new_dict

def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
    """

    ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
    :param args1:
    :param args2:
    :return:
    """
    # - the merged args
    # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
    merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
    args = argparse.Namespace(**merged_key_values_for_namespace)
    return args

def run_training(args_imported):
    args_default = parse_args()
    args = merge_args(args_default, args_imported)
    print(args)
    logging_dir = Path(args.output_dir, args.logging_dir)
    i=args.save_starting_step
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with="tensorboard",
        logging_dir=logging_dir,
    )

    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
        raise ValueError(
            "Gradient accumulation is not supported when training the text encoder in distributed training. "
            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
        )

    if args.seed is not None:
        set_seed(args.seed)

    if args.with_prior_preservation:
        class_images_dir = Path(args.class_data_dir)
        if not class_images_dir.exists():
            class_images_dir.mkdir(parents=True)
        cur_class_images = len(list(class_images_dir.iterdir()))

        if cur_class_images < args.num_class_images:
            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
            pipeline = StableDiffusionPipeline.from_pretrained(
                args.pretrained_model_name_or_path, torch_dtype=torch_dtype
            )
            pipeline.set_progress_bar_config(disable=True)

            num_new_images = args.num_class_images - cur_class_images
            logger.info(f"Number of class images to sample: {num_new_images}.")

            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)

            sample_dataloader = accelerator.prepare(sample_dataloader)
            pipeline.to(accelerator.device)

            for example in tqdm(
                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
            ):
                with torch.autocast("cuda"):                
                    images = pipeline(example["prompt"]).images

                for i, image in enumerate(images):
                    image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")

            del pipeline
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

    # 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 = 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 the tokenizer
    if args.tokenizer_name:
        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
    elif args.pretrained_model_name_or_path:
        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")

    # Load models and create wrapper for stable diffusion
    if args.train_only_unet:
      if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
      elif os.path.exists(str(args.output_dir+"/text_encoder")):
        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
      else:
        text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
    else:
      text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

    vae.requires_grad_(False)
    if not args.train_text_encoder:
        text_encoder.requires_grad_(False)

    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
        if args.train_text_encoder:
            text_encoder.gradient_checkpointing_enable()

    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 = (
        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.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,
    )

    noise_scheduler = DDPMScheduler(
        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
    )

    train_dataset = DreamBoothDataset(
        instance_data_root=args.instance_data_dir,
        instance_prompt=args.instance_prompt,
        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
        class_prompt=args.class_prompt,
        tokenizer=tokenizer,
        size=args.resolution,
        center_crop=args.center_crop,
        args=args,
    )

    def collate_fn(examples):
        input_ids = [example["instance_prompt_ids"] for example in examples]
        pixel_values = [example["instance_images"] for example in examples]

        # Concat class and instance examples for prior preservation.
        # We do this to avoid doing two forward passes.
        if args.with_prior_preservation:
            input_ids += [example["class_prompt_ids"] for example in examples]
            pixel_values += [example["class_images"] for example in examples]

        pixel_values = torch.stack(pixel_values)
        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()

        input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids

        batch = {
            "input_ids": input_ids,
            "pixel_values": pixel_values,
        }
        return batch

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

    # 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,
    )

    if args.train_text_encoder:
        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
        )
    else:
        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
            unet, optimizer, train_dataloader, lr_scheduler
        )

    weight_dtype = torch.float32
    if args.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif args.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    # Move text_encode and vae to gpu.
    # 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.
    vae.to(accelerator.device, dtype=weight_dtype)
    if not args.train_text_encoder:
        text_encoder.to(accelerator.device, dtype=weight_dtype)


    if args.cache_latents:
        latents_cache = []
        text_encoder_cache = []
        for batch in tqdm(train_dataloader, desc="Caching latents"):
            with torch.no_grad():
                batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
                batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
                latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
                if args.train_text_encoder:
                    text_encoder_cache.append(batch["input_ids"])
                else:
                    text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
        train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
        train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)

        del vae
        if not args.train_text_encoder:
            del text_encoder
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    # 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("dreambooth", config=vars(args))

    def bar(prg):
       br='|'+'█' * prg + ' ' * (25-prg)+'|'
       return br

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

    for epoch in range(args.num_train_epochs):
        unet.train()
        if args.train_text_encoder:
            text_encoder.train()
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(unet):
                # Convert images to latent space
                with torch.no_grad():
                    if args.cache_latents:
                        latents = batch[0][0]
                    else:
                        latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
                    latents = latents * 0.18215

                # 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
                if(args.cache_latents):
                    if args.train_text_encoder:
                        encoder_hidden_states = text_encoder(batch[0][1])[0]
                    else:
                        encoder_hidden_states = batch[0][1]
                else:
                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]

                # Predict the noise residual
                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
                
                # 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}")
                
                if args.with_prior_preservation:
                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                    target, target_prior = torch.chunk(target, 2, dim=0)

                    # Compute instance loss
                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()

                    # Compute prior loss
                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")

                    # Add the prior loss to the instance loss.
                    loss = loss + args.prior_loss_weight * prior_loss
                else:
                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()

                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = (
                        itertools.chain(unet.parameters(), text_encoder.parameters())
                        if args.train_text_encoder
                        else unet.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

            fll=round((global_step*100)/args.max_train_steps)
            fll=round(fll/4)
            pr=bar(fll)
            
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)
            progress_bar.set_description_str("Progress:"+pr)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

            if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
              if accelerator.is_main_process:
                print(" " +" Freezing the text_encoder ..."+" ")                
                frz_dir=args.output_dir + "/text_encoder_frozen"
                if os.path.exists(frz_dir):
                  subprocess.call('rm -r '+ frz_dir, shell=True)
                os.mkdir(frz_dir)
                pipeline = StableDiffusionPipeline.from_pretrained(
                    args.pretrained_model_name_or_path,
                    unet=accelerator.unwrap_model(unet),
                    text_encoder=accelerator.unwrap_model(text_encoder),
                )
                pipeline.text_encoder.save_pretrained(frz_dir)
                         
            if args.save_n_steps >= 200:
               if global_step < args.max_train_steps and global_step+1==i:
                  ckpt_name = "_step_" + str(global_step+1)
                  save_dir = Path(args.output_dir+ckpt_name)
                  save_dir=str(save_dir)
                  save_dir=save_dir.replace(" ", "_")                    
                  if not os.path.exists(save_dir):
                     os.mkdir(save_dir)
                  inst=save_dir[16:]
                  inst=inst.replace(" ", "_")
                  print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
                  # Create the pipeline using the trained modules and save it.
                  if accelerator.is_main_process:
                     pipeline = StableDiffusionPipeline.from_pretrained(
                           args.pretrained_model_name_or_path,
                           unet=accelerator.unwrap_model(unet),
                           text_encoder=accelerator.unwrap_model(text_encoder),
                     )
                     pipeline.save_pretrained(save_dir)
                     frz_dir=args.output_dir + "/text_encoder_frozen"                    
                     if args.train_text_encoder and os.path.exists(frz_dir):
                        subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
                        subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)                     
                     chkpth=args.Session_dir+"/"+inst+".ckpt"
                     subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
                     subprocess.call('rm -r '+ save_dir, shell=True)
                     i=i+args.save_n_steps
            
        accelerator.wait_for_everyone()

    # Create the pipeline using using the trained modules and save it.
    if accelerator.is_main_process:
      if args.dump_only_text_encoder:
         txt_dir=args.output_dir + "/text_encoder_trained"
         if not os.path.exists(txt_dir):
           os.mkdir(txt_dir)
         pipeline = StableDiffusionPipeline.from_pretrained(
             args.pretrained_model_name_or_path,
             unet=accelerator.unwrap_model(unet),
             text_encoder=accelerator.unwrap_model(text_encoder),
         )
         pipeline.text_encoder.save_pretrained(txt_dir)       

      elif args.train_only_unet:
        pipeline = StableDiffusionPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            unet=accelerator.unwrap_model(unet),
            text_encoder=accelerator.unwrap_model(text_encoder),
        )
        pipeline.save_pretrained(args.output_dir)
        txt_dir=args.output_dir + "/text_encoder_trained"
        subprocess.call('rm -r '+txt_dir, shell=True)
     
      else:
        pipeline = StableDiffusionPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            unet=accelerator.unwrap_model(unet),
            text_encoder=accelerator.unwrap_model(text_encoder),
        )
        frz_dir=args.output_dir + "/text_encoder_frozen"
        pipeline.save_pretrained(args.output_dir)
        if args.train_text_encoder and os.path.exists(frz_dir):
           subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
           subprocess.call('rm -r '+ frz_dir, shell=True) 

        if args.push_to_hub:
            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)

    accelerator.end_training()
    del pipeline
    torch.cuda.empty_cache()
    gc.collect()
if __name__ == "__main__":
    pass
    #main()