train_dreambooth_person_mask.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

# ADDED
from skimage.morphology import binary_dilation, binary_erosion
import cv2



import argparse
import copy
import gc
import importlib
import itertools
import logging
import math
import os
import shutil
import warnings
from pathlib import Path

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 ProjectConfiguration, set_seed
from huggingface_hub import create_repo, model_info, upload_folder
from huggingface_hub.utils import insecure_hashlib
from packaging import version
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    DiffusionPipeline,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module




if is_wandb_available():
    import wandb

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.28.0.dev0")

logger = get_logger(__name__)


def save_model_card(
    repo_id: str,
    images: list = None,
    base_model: str = None,
    train_text_encoder=False,
    prompt: str = None,
    repo_folder: str = None,
    pipeline: DiffusionPipeline = None,
):
    img_str = ""
    if images is not None:
        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"

    model_description = f"""
# DreamBooth - {repo_id}

This is a dreambooth model derived from {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following. \n
{img_str}

DreamBooth for the text encoder was enabled: {train_text_encoder}.
"""
    model_card = load_or_create_model_card(
        repo_id_or_path=repo_id,
        from_training=True,
        license="creativeml-openrail-m",
        base_model=base_model,
        prompt=prompt,
        model_description=model_description,
        inference=True,
    )

    tags = ["text-to-image", "dreambooth", "diffusers-training"]
    if isinstance(pipeline, StableDiffusionPipeline):
        tags.extend(["stable-diffusion", "stable-diffusion-diffusers"])
    else:
        tags.extend(["if", "if-diffusers"])
    model_card = populate_model_card(model_card, tags=tags)

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


def log_validation(
    text_encoder,
    tokenizer,
    unet,
    vae,
    args,
    accelerator,
    weight_dtype,
    global_step,
    prompt_embeds,
    negative_prompt_embeds,
):
    logger.info(
        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
        f" {args.validation_prompt}."
    )

    pipeline_args = {}

    if vae is not None:
        pipeline_args["vae"] = vae

    # create pipeline (note: unet and vae are loaded again in float32)
    pipeline = DiffusionPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        tokenizer=tokenizer,
        text_encoder=text_encoder,
        unet=unet,
        revision=args.revision,
        variant=args.variant,
        torch_dtype=weight_dtype,
        **pipeline_args,
    )

    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
    scheduler_args = {}

    if "variance_type" in pipeline.scheduler.config:
        variance_type = pipeline.scheduler.config.variance_type

        if variance_type in ["learned", "learned_range"]:
            variance_type = "fixed_small"

        scheduler_args["variance_type"] = variance_type

    module = importlib.import_module("diffusers")
    scheduler_class = getattr(module, args.validation_scheduler)
    pipeline.scheduler = scheduler_class.from_config(pipeline.scheduler.config, **scheduler_args)
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    if args.pre_compute_text_embeddings:
        pipeline_args = {
            "prompt_embeds": prompt_embeds,
            "negative_prompt_embeds": negative_prompt_embeds,
        }
    else:
        pipeline_args = {"prompt": args.validation_prompt}

    # run inference
    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
    images = []
    if args.validation_images is None:
        for _ in range(args.num_validation_images):
            with torch.autocast("cuda"):
                image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]
            images.append(image)
    else:
        for image in args.validation_images:
            image = Image.open(image)
            image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
            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("validation", np_images, global_step, 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()

    return images


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
    elif model_class == "T5EncoderModel":
        from transformers import T5EncoderModel

        return T5EncoderModel
    else:
        raise ValueError(f"{model_class} is not supported.")


# ADDED

def get_background_pixels_and_bbox(pil_img, blur_radius=0, bbox_padding=10, mask_parts=None):
    from PIL import Image, ImageFilter, ImageOps
    import numpy as np
    import mediapipe as mp
    if mask_parts is None:
        mask_parts = ["clothing"]
    elif isinstance(mask_parts, str):
        mask_parts = [mask_parts]

    mask_dict = {
        "background": 0,
        "hair": 1,
        "skin": 2,
        "face": 3,
        "clothing": 4,
        "others": 5
    }

    # Blur the image background based on the segmentation mask.
    image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.asarray(pil_img))
    options = mp.tasks.vision.ImageSegmenterOptions(
        base_options=mp.tasks.BaseOptions(model_asset_path="src/models/selfie_multiclass_256x256.tflite"),
        running_mode=mp.tasks.vision.RunningMode.IMAGE,
        output_category_mask=True)
    
    with mp.tasks.vision.ImageSegmenter.create_from_options(options) as segmenter:
        segmentation_result = segmenter.segment(image)
        combined_mask = np.zeros_like(segmentation_result.confidence_masks[0].numpy_view())

        for mask_part in mask_parts:
            if mask_part in mask_dict:
                body_mask = segmentation_result.confidence_masks[mask_dict[mask_part]].numpy_view()
                binary_mask = body_mask > 0.25
                combined_mask = np.logical_or(combined_mask, binary_mask)

        condition = np.stack((combined_mask,) * 3, axis=-1)
        mask = np.where(condition, 255, 0).astype(np.uint8)
        mask = Image.fromarray(mask)
        blurred_mask = mask.filter(ImageFilter.GaussianBlur(blur_radius)).convert("RGB")

        # Calculate bounding box (bbox) from the blurred_mask pixels
        mask_array = np.array(blurred_mask)[:,:,0] # Convert mask to a NumPy array and take one channel
        
        # Ensure that we are only considering non-zero values for our bbox calculation
        non_zero_indices = np.where(mask_array > 0) # Indices of non-zero elements
        if non_zero_indices[0].size > 0 and non_zero_indices[1].size > 0:
            y_min, y_max = non_zero_indices[0].min(), non_zero_indices[0].max()
            x_min, x_max = non_zero_indices[1].min(), non_zero_indices[1].max()
            
            width = x_max - x_min
            height = y_max - y_min
            
            # Determine the size of the square bbox
            bbox_size = max(width, height)
            
            # Calculate the amount of padding needed on each side
            x_padding = (bbox_size - width) // 2
            y_padding = (bbox_size - height) // 2
            
            # Update the bbox coordinates to make it a square
            x_min = max(0, x_min - x_padding)
            y_min = max(0, y_min - y_padding)
            x_max = min(mask_array.shape[1] - 1, x_max + x_padding + (bbox_size - width) % 2)
            y_max = min(mask_array.shape[0] - 1, y_max + y_padding + (bbox_size - height) % 2)
            
            
            bbox = (x_min, y_min, x_max, y_max)
        else:
            bbox = None
    
        #facemask = blurred_mask.crop(bbox)
        facemask = blurred_mask
        return facemask, bbox




def parse_args(input_args=None):
    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(
        "--variant",
        type=str,
        default=None,
        help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
    )
    parser.add_argument(
        "--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,
        required=True,
        help="The prompt with identifier specifying the instance",
    )
    parser.add_argument(
        "--class_prompt",
        type=str,
        default=None,
        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 there are not enough images already present in"
            " class_data_dir, additional images will be sampled with class_prompt."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="dreambooth-model",
        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",
        default=False,
        action="store_true",
        help=(
            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
            " cropped. The images will be resized to the resolution first before cropping."
        ),
    )
    parser.add_argument(
        "--train_text_encoder",
        action="store_true",
        help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
    )
    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(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
            "instructions."
        ),
    )
    parser.add_argument(
        "--checkpoints_total_limit",
        type=int,
        default=None,
        help=(
            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
            " for more details"
        ),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-6,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    parser.add_argument(
        "--validation_prompt",
        type=str,
        default=None,
        help="A prompt that is used during validation to verify that the model is learning.",
    )
    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_steps",
        type=int,
        default=100,
        help=(
            "Run validation every X steps. Validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`"
            " and logging the images."
        ),
    )
    parser.add_argument(
        "--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(
        "--prior_generation_precision",
        type=str,
        default=None,
        choices=["no", "fp32", "fp16", "bf16"],
        help=(
            "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument(
        "--set_grads_to_none",
        action="store_true",
        help=(
            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
            " behaviors, so disable this argument if it causes any problems. More info:"
            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
        ),
    )

    parser.add_argument(
        "--offset_noise",
        action="store_true",
        default=False,
        help=(
            "Fine-tuning against a modified noise"
            " See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information."
        ),
    )
    parser.add_argument(
        "--snr_gamma",
        type=float,
        default=None,
        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
        "More details here: https://arxiv.org/abs/2303.09556.",
    )
    parser.add_argument(
        "--pre_compute_text_embeddings",
        action="store_true",
        help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
    )
    parser.add_argument(
        "--tokenizer_max_length",
        type=int,
        default=None,
        required=False,
        help="The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.",
    )
    parser.add_argument(
        "--text_encoder_use_attention_mask",
        action="store_true",
        required=False,
        help="Whether to use attention mask for the text encoder",
    )
    parser.add_argument(
        "--skip_save_text_encoder", action="store_true", required=False, help="Set to not save text encoder"
    )
    parser.add_argument(
        "--validation_images",
        required=False,
        default=None,
        nargs="+",
        help="Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.",
    )
    parser.add_argument(
        "--class_labels_conditioning",
        required=False,
        default=None,
        help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
    )
    parser.add_argument(
        "--validation_scheduler",
        type=str,
        default="DPMSolverMultistepScheduler",
        choices=["DPMSolverMultistepScheduler", "DDPMScheduler"],
        help="Select which scheduler to use for validation. DDPMScheduler is recommended for DeepFloyd IF.",
    )

    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        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.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.")
    else:
        # logger is not available yet
        if args.class_data_dir is not None:
            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
        if args.class_prompt is not None:
            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")

    if args.train_text_encoder and args.pre_compute_text_embeddings:
        raise ValueError("`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`")

    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,
        class_data_root=None,
        class_prompt=None,
        class_num=None,
        size=512,
        center_crop=False,
        encoder_hidden_states=None,
        class_prompt_encoder_hidden_states=None,
        tokenizer_max_length=None,
    ):
        self.size = size
        self.center_crop = center_crop
        self.tokenizer = tokenizer
        self.encoder_hidden_states = encoder_hidden_states
        self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states
        self.tokenizer_max_length = tokenizer_max_length

        self.instance_data_root = Path(instance_data_root)
        if not self.instance_data_root.exists():
            raise ValueError(f"Instance {self.instance_data_root} 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 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())
            if class_num is not None:
                self.num_class_images = min(len(self.class_images_path), class_num)
            else:
                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]),
            ]
        )

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

        self.image_transforms_norm = transforms.Compose(
            [
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        self.image_transforms_mask = transforms.Compose(
            [
                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.ToTensor(),
            ]
        )



    def __len__(self):
        return self._length

    def __getitem__(self, index):
        example = {}
        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
        instance_image = exif_transpose(instance_image)



        

        

        # ADDED

        # weigh the clothes less so we want the strenghth to be less for clothes
        # 1.0 for face an skin
        # < 1.0 for clothes
        # 0.3 too large
        # 0.05 good, but still fits too much @ 1500 steps
        # 0.01 still fits and messes up face since we eliminate too many neurons
        # lets go back to 0.05, since it generated good faces.


        # # (adjustment: make it completely binary, we will remove then replace with nosie like we did with BG)
        # clothes_scale = 1

        # adjustment V2, keep it back at 0.05, because moving it to 1 also changes the mask we multiply with.
        clothes_scale = 0.05
        
        facemask, bbox = get_background_pixels_and_bbox(instance_image, mask_parts=['hair', 'skin', 'face'], bbox_padding=0)
        clothesmask, bbox = get_background_pixels_and_bbox(instance_image, mask_parts=['clothing'], bbox_padding=0)


        clothesmask_array = np.array(clothesmask)
        facemask_array = np.array(facemask)
        facemask = Image.fromarray(clothesmask_array + facemask_array)
        facemask_tensor = self.image_transforms_mask(facemask)
        
        # don't wanna norm yet we'll do it after applying our noise cuz it could mess things up
        instance_image_tensor = self.image_transforms_no_norm(instance_image)
        
        # ERODING FACE MASK
        struct_elem = np.ones((6, 6), dtype=np.bool_)
        mask_array = np.any(np.array(facemask) != [0, 0, 0], axis=-1)
        eroded_mask = binary_erosion(mask_array, struct_elem)
        
        # Set clothing regions to gray (eroding it sets it to white)
        # probably could be optimized
        # binary erosion causes the mask to be completely white and completely black (binary)
        # so can't put in the gray clothes white face mask, instead we make clothes gray after
        # So we pass in the combined mask of face + clothes to get our eroded mask, then
        # create a new mask of pixels in both eroded mask and original face mask, set to white
        # and also create a new mask of pixels in both eroded mask and original clothes mask, set to gray
        # then add these two to get eroded mask of gray clothes white face
        eroded_clothes = np.zeros_like(np.array(instance_image))
        eroded_clothes[eroded_mask & (np.any(clothesmask_array != 0, axis=-1))] = [255 * clothes_scale, 255 * clothes_scale, 255 * clothes_scale]

        eroded_clothes_binary = np.zeros_like(np.array(instance_image))
        eroded_clothes_binary[eroded_mask & (np.any(clothesmask_array != 0, axis=-1))] = [255, 255, 255]


        
        eroded_face = np.zeros_like(np.array(instance_image))
        eroded_face[eroded_mask & (np.any(facemask_array != 0, axis=-1))] = [255, 255, 255]
        combined_mask = eroded_clothes + eroded_face
        combined_mask_image = Image.fromarray(combined_mask)
        combined_mask_tensor = self.image_transforms_mask(combined_mask_image)
                
        # BLUR THE EDGES OF THE COMBINED MASK
        blurred_combined_mask = np.copy(combined_mask)
        gray_image = cv2.cvtColor(blurred_combined_mask, cv2.COLOR_RGB2GRAY)
        edges = cv2.Canny(gray_image, threshold1=100, threshold2=200)
        edge_mask = edges > 0
        blurred_edges = cv2.GaussianBlur(blurred_combined_mask, (5, 5), sigmaX=0)
        blurred_combined_mask[edge_mask] = blurred_edges[edge_mask]
        blurred_combined_mask_image = Image.fromarray(blurred_combined_mask)
        blurred_combined_mask_tensor = self.image_transforms_mask(blurred_combined_mask_image)

        
        # ALSO BLUR THE ERODED CLOTHES MASK (JUST HORIZONTALLY)
        blurred_clothes_mask = np.copy(eroded_clothes_binary)
        kernel_size = (1, 5)  # (height, width) - height is 1 for horizontal blur
        blurred_clothes_mask = cv2.GaussianBlur(blurred_clothes_mask, kernel_size, sigmaX=0)
        blurred_clothes_mask_image = Image.fromarray(blurred_clothes_mask)
        blurred_clothes_mask_tensor = self.image_transforms_mask(blurred_clothes_mask_image)


        # # visualize to ensure these ops are being performed correctly
        # visualization_folder = "visualization_images"
        # os.makedirs(visualization_folder, exist_ok=True)
        # save_image(blurred_clothes_mask_tensor, os.path.join(visualization_folder, f"clothes_mask_blurring.png"))
        # save_image(blurred_combined_mask_tensor, os.path.join(visualization_folder, f"facemask_blurring.png"))
        # save_image(combined_mask_tensor, os.path.join(visualization_folder, f"facemask_erosion.png"))
        # save_image(facemask_tensor, os.path.join(visualization_folder, f"facemask.png"))
        
        example["clothes_masks"] = blurred_clothes_mask_tensor
        example["instance_images"] = instance_image_tensor
        example["blurred_masks"] = blurred_combined_mask_tensor
        example["masks"] = combined_mask_tensor




    
        
    
        # Add instance prompt IDs and attention mask
        instance_prompt = self.instance_prompt
        instance_text_inputs = self.tokenizer(
            instance_prompt,
            padding="max_length",
            truncation=True,
            max_length=self.tokenizer.model_max_length,
            return_tensors="pt",
        )
        example["instance_prompt_ids"] = instance_text_inputs.input_ids
        example["instance_attention_mask"] = instance_text_inputs.attention_mask
    
        if self.class_data_root:
            class_image = Image.open(self.class_images_path[index % self.num_class_images])
            class_image = exif_transpose(class_image)
            if not class_image.mode == "RGB":
                class_image = class_image.convert("RGB")
            example["class_images"] = self.image_transforms(class_image)
    
            # Add class prompt IDs and attention mask
            class_prompt = self.class_prompt
            class_text_inputs = self.tokenizer(
                class_prompt,
                padding="max_length",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            )
            example["class_prompt_ids"] = class_text_inputs.input_ids
            example["class_attention_mask"] = class_text_inputs.attention_mask
    
        return example



import os
import torchvision.transforms as transforms
from torchvision.utils import save_image

def collate_fn(examples, with_prior_preservation=False, save_dir="processed_images"):
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)  # Create save directory if it doesn't exist

    has_attention_mask = "instance_attention_mask" in examples[0]
    input_ids = [example["instance_prompt_ids"] for example in examples]
    pixel_values = [example["instance_images"] for example in examples]
    

    if has_attention_mask:
        attention_mask = [example["instance_attention_mask"] for example in examples]

    if with_prior_preservation:
        input_ids += [example["class_prompt_ids"] for example in examples]
        pixel_values += [example["class_images"] for example in examples]
        if has_attention_mask:
            attention_mask += [example["class_attention_mask"] for example in examples]

    pixel_values = torch.stack(pixel_values)

    
    input_ids = torch.cat(input_ids, dim=0)

    

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

        # ADDED
        "blurred_masks": torch.stack([example["blurred_masks"] for example in examples]),
        "clothes_masks": torch.stack([example["clothes_masks"] for example in examples]),
        "masks": torch.stack([example["masks"] for example in examples])
    }

    if has_attention_mask:
        attention_mask = torch.cat(attention_mask, dim=0)
        batch["attention_mask"] = attention_mask

    return batch






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


def model_has_vae(args):
    config_file_name = os.path.join("vae", AutoencoderKL.config_name)
    if os.path.isdir(args.pretrained_model_name_or_path):
        config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)
        return os.path.isfile(config_file_name)
    else:
        files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings
        return any(file.rfilename == config_file_name for file in files_in_repo)


def tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):
    if tokenizer_max_length is not None:
        max_length = tokenizer_max_length
    else:
        max_length = tokenizer.model_max_length

    text_inputs = tokenizer(
        prompt,
        truncation=True,
        padding="max_length",
        max_length=max_length,
        return_tensors="pt",
    )

    return text_inputs








def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):
    text_input_ids = input_ids.to(text_encoder.device)

    if text_encoder_use_attention_mask:
        attention_mask = attention_mask.to(text_encoder.device)
    else:
        attention_mask = None

    prompt_embeds = text_encoder(
        text_input_ids,
        attention_mask=attention_mask,
        return_dict=False,
    )
    prompt_embeds = prompt_embeds[0]

    return prompt_embeds


def main(args):
    if args.report_to == "wandb" and args.hub_token is not None:
        raise ValueError(
            "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
            " Please use `huggingface-cli login` to authenticate with the Hub."
        )

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

    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=args.report_to,
        project_config=accelerator_project_config,
    )

    # Disable AMP for MPS.
    if torch.backends.mps.is_available():
        accelerator.native_amp = False

    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.")

    # 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."
        )

    # 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)

    # Generate class images if prior preservation is enabled.
    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
            if args.prior_generation_precision == "fp32":
                torch_dtype = torch.float32
            elif args.prior_generation_precision == "fp16":
                torch_dtype = torch.float16
            elif args.prior_generation_precision == "bf16":
                torch_dtype = torch.bfloat16
            pipeline = DiffusionPipeline.from_pretrained(
                args.pretrained_model_name_or_path,
                torch_dtype=torch_dtype,
                safety_checker=None,
                revision=args.revision,
                variant=args.variant,
            )
            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
            ):
                images = pipeline(example["prompt"]).images

                for i, image in enumerate(images):
                    hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                    image.save(image_filename)

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

    # 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
    )

    if model_has_vae(args):
        vae = AutoencoderKL.from_pretrained(
            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
        )
    else:
        vae = None

    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
    )

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

    # 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:
            for model in models:
                sub_dir = "unet" if isinstance(model, type(unwrap_model(unet))) else "text_encoder"
                model.save_pretrained(os.path.join(output_dir, sub_dir))

                # make sure to pop weight so that corresponding model is not saved again
                weights.pop()

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

            if isinstance(model, type(unwrap_model(text_encoder))):
                # load transformers style into model
                load_model = text_encoder_cls.from_pretrained(input_dir, subfolder="text_encoder")
                model.config = load_model.config
            else:
                # load diffusers style into model
                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
                model.register_to_config(**load_model.config)

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

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

    if vae is not None:
        vae.requires_grad_(False)

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

    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warning(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

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

    # 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(unet).dtype != torch.float32:
        raise ValueError(f"Unet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}")

    if args.train_text_encoder and unwrap_model(text_encoder).dtype != torch.float32:
        raise ValueError(
            f"Text encoder loaded as datatype {unwrap_model(text_encoder).dtype}." f" {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 = (
        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,
    )

    if args.pre_compute_text_embeddings:

        def compute_text_embeddings(prompt):
            with torch.no_grad():
                text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)
                prompt_embeds = encode_prompt(
                    text_encoder,
                    text_inputs.input_ids,
                    text_inputs.attention_mask,
                    text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                )

            return prompt_embeds

        pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
        validation_prompt_negative_prompt_embeds = compute_text_embeddings("")

        if args.validation_prompt is not None:
            validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)
        else:
            validation_prompt_encoder_hidden_states = None

        if args.class_prompt is not None:
            pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)
        else:
            pre_computed_class_prompt_encoder_hidden_states = None

        text_encoder = None
        tokenizer = None

        gc.collect()
        torch.cuda.empty_cache()
    else:
        pre_computed_encoder_hidden_states = None
        validation_prompt_encoder_hidden_states = None
        validation_prompt_negative_prompt_embeds = None
        pre_computed_class_prompt_encoder_hidden_states = None

    # Dataset and DataLoaders creation:
    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,
        class_num=args.num_class_images,
        tokenizer=tokenizer,
        size=args.resolution,
        center_crop=args.center_crop,
        encoder_hidden_states=pre_computed_encoder_hidden_states,
        class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,
        tokenizer_max_length=args.tokenizer_max_length,
    )

    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=args.train_batch_size,
        shuffle=True,
        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
        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`.
    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
        )

    # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
    # as these weights 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 and text_encoder to device and cast to weight_dtype
    if vae is not None:
        vae.to(accelerator.device, dtype=weight_dtype)

    if not args.train_text_encoder and text_encoder is not None:
        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 = vars(copy.deepcopy(args))
        tracker_config.pop("validation_images")
        accelerator.init_trackers("dreambooth", config=tracker_config)

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

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

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

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

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

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








    
    # ADDED

    
    def process_single_image(instance_image_tensor, blurred_mask, clothes_mask):
        # instance_image_tensor and facemask tensor has H,W 512x512 with vals between 0 and 1

        # facemask has already been eroded and blurred in getitem

        # all we will do is alter the instance image, we want to generate noise in the BG
        # 1. BG mask is 1 - facemask
        # 2. generate BG noise
        # 3. facemask * instance_image_tensor + noise * BG mask
        

        # BG MASK
        background_mask = 1 - blurred_mask

        # BG Noise
        noise = torch.randn_like(instance_image_tensor)
        noise_min = noise.min()
        noise_max = noise.max()
        # Normalize the noise between 0 and 1
        background_noise = (noise - noise_min) / (noise_max - noise_min)

        # tried 0.5, 0.9, 0.1
        # 0.01 alright @ 1500 steps
        noise_scale = 0.004
        background_noise = background_noise * noise_scale

        # Ops
        instance_image_tensor = instance_image_tensor * blurred_mask.float() + background_noise * background_mask.float()



        # ADD NOISE TO CLOTHES
        
        # generate random noise between -0.5 and 0.5
        # (since we are putting it on top of existing values, we don't just want to add, we want to subtract too
        #clothes_noise = torch.rand_like(instance_image_tensor) - 0.5

        # actually, the intial idea was to preserve some of the details, but turns out looking at the processed
        # images its just better to basically compeltely remove and replace with masked noise
        # so let's just add instead of subtract.
        
        clothes_noise = torch.randn_like(instance_image_tensor)
        clothes_noise_min = noise.min()
        clothes_noise_max = noise.max()

        # Normalize the noise between 0 and 1
        clothes_noise = (clothes_noise - clothes_noise_min) / (clothes_noise_max - clothes_noise_min)

        # basically do the same with the BG
        # for clothes mask of 1, we have clothes_scale = 0.004



        # TEST
        # maybe we want to bring it up, since keeping it here means that
        # since we are generating UNIFORM random noise between 0 and 1, basically all the noise
        # that does not have a value of 1 are set to <0.081 * 0.05 * 255 = 0.004 * 255 = 1
        # so essentially black pixels. Worked though for BG, wonder why.



        clothes_noise_scale = 0.0045
        clothes_noise = clothes_noise * clothes_noise_scale

        
        # Ops
        # set clothing regions to 0, then add noise
        instance_image_tensor = instance_image_tensor * (1-clothes_mask) + (clothes_noise * clothes_mask)


        # don't need to clamp anymore since we just adding
        # # then clamp the values (since we subtract + add onto exisitng values, it could go below 0 or above 1)
        # instance_image_tensor = torch.clamp(instance_image_tensor, min=0, max=1)


        visualization_folder = "visualization_images"
        os.makedirs(visualization_folder, exist_ok=True)
        save_image(instance_image_tensor, os.path.join(visualization_folder, f"process_image.png"))

        

        # then we can norm between -1 and 1 instead of 0,1
        instance_image_tensor = (instance_image_tensor - 0.5) * 2
        
        return instance_image_tensor
    








    
    for epoch in range(first_epoch, 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):


                # ADDED

                # now let's perform the operations here
                pixel_values = batch["pixel_values"]
                blurred_masks = batch["blurred_masks"]
                masks = batch["masks"]
                clothes_masks = batch["clothes_masks"]

                processed_images = []
                for i in range(pixel_values.size(0)):
                    # (if you want blur, pass in blurred masks instead of masks)
                    processed_image = process_single_image(pixel_values[i], blurred_masks[i], clothes_masks[i]) #masks[i])
                    processed_images.append(processed_image)
                
                pixel_values = torch.stack(processed_images)



                if vae is not None:
                    model_input = vae.encode(pixel_values.to(dtype=weight_dtype)).latent_dist.sample()
                    model_input = model_input * vae.config.scaling_factor



                else:
                    model_input = pixel_values


    
                # Sample noise that we'll add to the model input
                if args.offset_noise:
                    noise = torch.randn_like(model_input) + 0.1 * torch.randn(
                        model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device
                    )
                else:
                    noise = torch.randn_like(model_input)
                bsz, channels, height, width = model_input.shape
                # Sample a random timestep for each image
                timesteps = torch.randint(
                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
                )
                timesteps = timesteps.long()
    
                # Add noise to the model input according to the noise magnitude at each timestep
                # (this is the forward diffusion process)
                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
    
                # Get the text embedding for conditioning
                if args.pre_compute_text_embeddings:
                    encoder_hidden_states = batch["input_ids"]
                else:
                    encoder_hidden_states = encode_prompt(
                        text_encoder,
                        batch["input_ids"],
                        batch["attention_mask"],
                        text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                    )
    
                if unwrap_model(unet).config.in_channels == channels * 2:
                    noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)
    
                if args.class_labels_conditioning == "timesteps":
                    class_labels = timesteps
                else:
                    class_labels = None
    
                # Predict the noise residual
                model_pred = unet(
                    noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels, return_dict=False
                )[0]
    
                if model_pred.shape[1] == 6:
                    model_pred, _ = torch.chunk(model_pred, 2, dim=1)
    
                # 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(model_input, 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 prior loss
                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
    

                # Compute instance loss
                if args.snr_gamma is None:



                    # ADDED
                    
    
                    

                    # don't use mask in loss becasue using of the VAE thing it wouldn’t really make a difference as 
                    # spatial information is not preserved when encoding the mask. So using a mask when calculating
                    # the loss just reduces the number of weights the model knows are actually impacting the loss,
                    # since we don’t know what the weights actually represent.
                    
                    loss = (model_pred - target).pow(2) # * masks
                    loss = loss.mean()
                    
                
                else:
                    # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
                    # Since we predict the noise instead of x_0, the original formulation is slightly changed.
                    # This is discussed in Section 4.2 of the same paper.
                    snr = compute_snr(noise_scheduler, timesteps)
                    base_weight = (
                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
                    )
    
                    if noise_scheduler.config.prediction_type == "v_prediction":
                        # Velocity objective needs to be floored to an SNR weight of one.
                        mse_loss_weights = base_weight + 1
                    else:
                        # Epsilon and sample both use the same loss weights.
                        mse_loss_weights = base_weight
                    
                    # old and won't use
                    masked_loss = F.mse_loss(model_pred * new_encoded_masks, target * new_encoded_masks, reduction="none")
                    masked_loss = masked_loss.mean(dim=list(range(1, len(masked_loss.shape))))
                    loss = (masked_loss * mse_loss_weights).mean()
    
                if args.with_prior_preservation:
                    # Add the prior loss to the instance loss.
                    loss = loss + args.prior_loss_weight * prior_loss
                
                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(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)
                        logger.info(f"Saved state to {save_path}")

                    images = []

                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                        images = log_validation(
                            unwrap_model(text_encoder) if text_encoder is not None else text_encoder,
                            tokenizer,
                            unwrap_model(unet),
                            vae,
                            args,
                            accelerator,
                            weight_dtype,
                            global_step,
                            validation_prompt_encoder_hidden_states,
                            validation_prompt_negative_prompt_embeds,
                        )

            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 the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        pipeline_args = {}

        if text_encoder is not None:
            pipeline_args["text_encoder"] = unwrap_model(text_encoder)

        if args.skip_save_text_encoder:
            pipeline_args["text_encoder"] = None

        pipeline = DiffusionPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            unet=unwrap_model(unet),
            revision=args.revision,
            variant=args.variant,
            **pipeline_args,
        )

        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
        scheduler_args = {}

        if "variance_type" in pipeline.scheduler.config:
            variance_type = pipeline.scheduler.config.variance_type

            if variance_type in ["learned", "learned_range"]:
                variance_type = "fixed_small"

            scheduler_args["variance_type"] = variance_type

        pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)

        pipeline.save_pretrained(args.output_dir)

        if args.push_to_hub:
            save_model_card(
                repo_id,
                images=images,
                base_model=args.pretrained_model_name_or_path,
                train_text_encoder=args.train_text_encoder,
                prompt=args.instance_prompt,
                repo_folder=args.output_dir,
                pipeline=pipeline,
            )
            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )

    accelerator.end_training()


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