repo_description: Official implementation for "Break-A-Scene: Extracting Multiple Concepts from a Single Image" [SIGGRAPH Asia 2023]
repo_topics: deep-learning diffusion-models generative-ai multimodal text-to-image
code: """
  Copyright 2023 Google LLC
  
  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
  
      https://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
  limitations under the License.
  """
  
  import argparse
  import hashlib
  import itertools
  import logging
  import math
  import os
  import warnings
  from pathlib import Path
  from typing import List, Optional
  import random
  
  import torch
  import torch.nn.functional as F
  import torchvision.transforms.functional as TF
  import torch.utils.checkpoint
  from torch.utils.data import Dataset
  import numpy as np
  
  import datasets
  import diffusers
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from diffusers import (
      AutoencoderKL,
      DDPMScheduler,
      DiffusionPipeline,
      UNet2DConditionModel,
      DDIMScheduler,
  )
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version
  from diffusers.utils.import_utils import is_xformers_available
  from huggingface_hub import HfFolder, Repository, create_repo, whoami
  from PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import ptp_utils
  from ptp_utils import AttentionStore
  from diffusers.models.cross_attention import CrossAttention
  
  check_min_version("0.12.0")
  
  logger = get_logger(__name__)
  
  
  def import_model_class_from_model_name_or_path(
      pretrained_model_name_or_path: str, revision: str
  ):
      text_encoder_config = PretrainedConfig.from_pretrained(
          pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=revision,
      )
      model_class = text_encoder_config.architectures[0]
  
      if model_class == "CLIPTextModel":
          from transformers import CLIPTextModel
  
          return CLIPTextModel
      elif model_class == "RobertaSeriesModelWithTransformation":
          from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
              RobertaSeriesModelWithTransformation,
          )
  
          return RobertaSeriesModelWithTransformation
      else:
          raise ValueError(f"{model_class} is not supported.")
  
  
  def 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="stabilityai/stable-diffusion-2-1-base",
          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. Trainable model components should be"
              " float32 precision."
          ),
      )
      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(
          "--class_prompt",
          type=str,
          default="a photo at the beach",
          help="The prompt to specify images in the same class as provided instance images.",
      )
      parser.add_argument(
          "--no_prior_preservation",
          action="store_false",
          help="Flag to add prior preservation loss.",
          dest="with_prior_preservation"
      )
      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="outputs",
          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(
          "--no_train_text_encoder",
          action="store_false",
          help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
          dest="train_text_encoder"
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=1,
          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(
          "--phase1_train_steps",
          type=int,
          default="400",
          help="Number of trainig steps for the first phase.",
      )
      parser.add_argument(
          "--phase2_train_steps",
          type=int,
          default="400",
          help="Number of trainig steps for the second phase.",
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=int,
          default=5000,
          help=(
              "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
              " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
              " training using `--resume_from_checkpoint`."
          ),
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help=(
              "Whether training should be resumed from a previous checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
          ),
      )
      parser.add_argument(
          "--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=2e-6,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--initial_learning_rate",
          type=float,
          default=5e-4,
          help="The LR for the Textual Inversion steps.",
      )
      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=0,
          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(
          "--hub_token",
          type=str,
          default=None,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          default=None,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--logging_dir",
          type=str,
          default="logs",
          help=(
              "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
          ),
      )
      parser.add_argument(
          "--allow_tf32",
          action="store_true",
          help=(
              "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
              " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
          ),
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="tensorboard",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
              ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
          ),
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default="fp16",
          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("--lambda_attention", type=float, default=1e-2)
      parser.add_argument("--img_log_steps", type=int, default=200)
      parser.add_argument("--num_of_assets", type=int, default=1)
      parser.add_argument("--initializer_tokens", type=str, nargs="+", default=[])
      parser.add_argument(
          "--placeholder_token",
          type=str,
          default="<asset>",
          help="A token to use as a placeholder for the concept.",
      )
      parser.add_argument(
          "--do_not_apply_masked_loss",
          action="store_false",
          help="Use masked loss instead of standard epsilon prediciton loss",
          dest="apply_masked_loss"
      )
      parser.add_argument(
          "--log_checkpoints",
          action="store_true",
          help="Indicator to log intermediate model checkpoints",
      )
  
      if input_args is not None:
          args = parser.parse_args(input_args)
      else:
          args = parser.parse_args()
  
      assert len(args.initializer_tokens) == 0 or len(args.initializer_tokens) == args.num_of_assets
      args.max_train_steps = args.phase1_train_steps + args.phase2_train_steps
  
      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."
              )
  
      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,
          placeholder_tokens,
          tokenizer,
          class_data_root=None,
          class_prompt=None,
          size=512,
          center_crop=False,
          num_of_assets=1,
          flip_p=0.5,
      ):
          self.size = size
          self.center_crop = center_crop
          self.tokenizer = tokenizer
          self.flip_p = flip_p
  
          self.image_transforms = transforms.Compose(
              [
                  transforms.ToTensor(),
                  transforms.Normalize([0.5], [0.5]),
              ]
          )
          self.mask_transforms = transforms.Compose(
              [
                  transforms.ToTensor(),
              ]
          )
  
          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.placeholder_tokens = placeholder_tokens
  
          instance_img_path = os.path.join(instance_data_root, "img.jpg")
          self.instance_image = self.image_transforms(Image.open(instance_img_path))
  
          self.instance_masks = []
          for i in range(num_of_assets):
              instance_mask_path = os.path.join(instance_data_root, f"mask{i}.png")
              curr_mask = Image.open(instance_mask_path)
              curr_mask = self.mask_transforms(curr_mask)[0, None, None, ...]
              self.instance_masks.append(curr_mask)
          self.instance_masks = torch.cat(self.instance_masks)
  
          self._length = 1
  
          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())
              self.num_class_images = len(self.class_images_path)
              self._length = max(self.num_class_images, self._length)
              self.class_prompt = class_prompt
          else:
              self.class_data_root = None
  
      def __len__(self):
          return self._length
  
      def __getitem__(self, index):
          example = {}
  
          num_of_tokens = random.randrange(1, len(self.placeholder_tokens) + 1)
          tokens_ids_to_use = random.sample(
              range(len(self.placeholder_tokens)), k=num_of_tokens
          )
          tokens_to_use = [self.placeholder_tokens[tkn_i] for tkn_i in tokens_ids_to_use]
          prompt = "a photo of " + " and ".join(tokens_to_use)
  
          example["instance_images"] = self.instance_image
          example["instance_masks"] = self.instance_masks[tokens_ids_to_use]
          example["token_ids"] = torch.tensor(tokens_ids_to_use)
  
          if random.random() > self.flip_p:
              example["instance_images"] = TF.hflip(example["instance_images"])
              example["instance_masks"] = TF.hflip(example["instance_masks"])
  
          example["instance_prompt_ids"] = self.tokenizer(
              prompt,
              truncation=True,
              padding="max_length",
              max_length=self.tokenizer.model_max_length,
              return_tensors="pt",
          ).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,
                  truncation=True,
                  padding="max_length",
                  max_length=self.tokenizer.model_max_length,
                  return_tensors="pt",
              ).input_ids
  
          return example
  
  
  def collate_fn(examples, with_prior_preservation=False):
      input_ids = [example["instance_prompt_ids"] for example in examples]
      pixel_values = [example["instance_images"] for example in examples]
      masks = [example["instance_masks"] for example in examples]
      token_ids = [example["token_ids"] for example in examples]
  
      if with_prior_preservation:
          input_ids = [example["class_prompt_ids"] for example in examples] + input_ids
          pixel_values = [example["class_images"] for example in examples] + pixel_values
  
      pixel_values = torch.stack(pixel_values)
      pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
  
      input_ids = torch.cat(input_ids, dim=0)
      masks = torch.stack(masks)
      token_ids = torch.stack(token_ids)
  
      batch = {
          "input_ids": input_ids,
          "pixel_values": pixel_values,
          "instance_masks": masks,
          "token_ids": token_ids,
      }
      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 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}"
  
  
  class SpatialDreambooth:
      def __init__(self):
          self.args = parse_args()
          self.main()
  
      def main(self):
          logging_dir = Path(self.args.output_dir, self.args.logging_dir)
  
          self.accelerator = Accelerator(
              gradient_accumulation_steps=self.args.gradient_accumulation_steps,
              mixed_precision=self.args.mixed_precision,
              log_with=self.args.report_to,
              logging_dir=logging_dir,
          )
  
          if (
              self.args.train_text_encoder
              and self.args.gradient_accumulation_steps > 1
              and self.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."
              )
  
          logging.basicConfig(
              format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
              datefmt="%m/%d/%Y %H:%M:%S",
              level=logging.INFO,
          )
          logger.info(self.accelerator.state, main_process_only=False)
          if self.accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
              diffusers.utils.logging.set_verbosity_info()
          else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
              diffusers.utils.logging.set_verbosity_error()
  
          # If passed along, set the training seed now.
          if self.args.seed is not None:
              set_seed(self.args.seed)
  
          # Generate class images if prior preservation is enabled.
          if self.args.with_prior_preservation:
              class_images_dir = Path(self.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 < self.args.num_class_images:
                  torch_dtype = (
                      torch.float16
                      if self.accelerator.device.type == "cuda"
                      else torch.float32
                  )
                  if self.args.prior_generation_precision == "fp32":
                      torch_dtype = torch.float32
                  elif self.args.prior_generation_precision == "fp16":
                      torch_dtype = torch.float16
                  elif self.args.prior_generation_precision == "bf16":
                      torch_dtype = torch.bfloat16
                  pipeline = DiffusionPipeline.from_pretrained(
                      self.args.pretrained_model_name_or_path,
                      torch_dtype=torch_dtype,
                      safety_checker=None,
                      revision=self.args.revision,
                  )
                  pipeline.set_progress_bar_config(disable=True)
  
                  num_new_images = self.args.num_class_images - cur_class_images
                  logger.info(f"Number of class images to sample: {num_new_images}.")
  
                  sample_dataset = PromptDataset(self.args.class_prompt, num_new_images)
                  sample_dataloader = torch.utils.data.DataLoader(
                      sample_dataset, batch_size=self.args.sample_batch_size
                  )
  
                  sample_dataloader = self.accelerator.prepare(sample_dataloader)
                  pipeline.to(self.accelerator.device)
  
                  for example in tqdm(
                      sample_dataloader,
                      desc="Generating class images",
                      disable=not self.accelerator.is_local_main_process,
                  ):
                      images = pipeline(example["prompt"]).images
  
                      for i, image in enumerate(images):
                          hash_image = 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 self.accelerator.is_main_process:
              os.makedirs(self.args.output_dir, exist_ok=True)
  
          # import correct text encoder class
          text_encoder_cls = import_model_class_from_model_name_or_path(
              self.args.pretrained_model_name_or_path, self.args.revision
          )
  
          # Load scheduler and models
          self.noise_scheduler = DDPMScheduler.from_pretrained(
              self.args.pretrained_model_name_or_path, subfolder="scheduler"
          )
          self.text_encoder = text_encoder_cls.from_pretrained(
              self.args.pretrained_model_name_or_path,
              subfolder="text_encoder",
              revision=self.args.revision,
          )
          self.vae = AutoencoderKL.from_pretrained(
              self.args.pretrained_model_name_or_path,
              subfolder="vae",
              revision=self.args.revision,
          )
          self.unet = UNet2DConditionModel.from_pretrained(
              self.args.pretrained_model_name_or_path,
              subfolder="unet",
              revision=self.args.revision,
          )
  
          # Load the tokenizer
          if self.args.tokenizer_name:
              self.tokenizer = AutoTokenizer.from_pretrained(
                  self.args.tokenizer_name, revision=self.args.revision, use_fast=False
              )
          elif self.args.pretrained_model_name_or_path:
              self.tokenizer = AutoTokenizer.from_pretrained(
                  self.args.pretrained_model_name_or_path,
                  subfolder="tokenizer",
                  revision=self.args.revision,
                  use_fast=False,
              )
  
          # Add assets tokens to tokenizer
          self.placeholder_tokens = [
              self.args.placeholder_token.replace(">", f"{idx}>")
              for idx in range(self.args.num_of_assets)
          ]
          num_added_tokens = self.tokenizer.add_tokens(self.placeholder_tokens)
          assert num_added_tokens == self.args.num_of_assets
          self.placeholder_token_ids = self.tokenizer.convert_tokens_to_ids(
              self.placeholder_tokens
          )
          self.text_encoder.resize_token_embeddings(len(self.tokenizer))
          self.args.instance_prompt = "a photo of " + " and ".join(
              self.placeholder_tokens
          )
  
          if len(self.args.initializer_tokens) > 0:
              # Use initializer tokens
              token_embeds = self.text_encoder.get_input_embeddings().weight.data
              for tkn_idx, initializer_token in enumerate(self.args.initializer_tokens):
                  curr_token_ids = self.tokenizer.encode(
                      initializer_token, add_special_tokens=False
                  )
                  # assert (len(curr_token_ids)) == 1
                  token_embeds[self.placeholder_token_ids[tkn_idx]] = token_embeds[
                      curr_token_ids[0]
                  ]
          else:
              # Initialize new tokens randomly
              token_embeds = self.text_encoder.get_input_embeddings().weight.data
              token_embeds[-self.args.num_of_assets :] = token_embeds[
                  -3 * self.args.num_of_assets : -2 * self.args.num_of_assets
              ]
  
          # Set validation scheduler for logging
          self.validation_scheduler = DDIMScheduler(
              beta_start=0.00085,
              beta_end=0.012,
              beta_schedule="scaled_linear",
              clip_sample=False,
              set_alpha_to_one=False,
          )
          self.validation_scheduler.set_timesteps(50)
  
          # We start by only optimizing the embeddings
          self.vae.requires_grad_(False)
          self.unet.requires_grad_(False)
          # Freeze all parameters except for the token embeddings in text encoder
          self.text_encoder.text_model.encoder.requires_grad_(False)
          self.text_encoder.text_model.final_layer_norm.requires_grad_(False)
          self.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
  
          if self.args.enable_xformers_memory_efficient_attention:
              if is_xformers_available():
                  self.unet.enable_xformers_memory_efficient_attention()
              else:
                  raise ValueError(
                      "xformers is not available. Make sure it is installed correctly"
                  )
  
          if self.args.gradient_checkpointing:
              self.unet.enable_gradient_checkpointing()
              if self.args.train_text_encoder:
                  self.text_encoder.gradient_checkpointing_enable()
  
          if self.args.allow_tf32:
              torch.backends.cuda.matmul.allow_tf32 = True
  
          if self.args.scale_lr:
              self.args.learning_rate = (
                  self.args.learning_rate
                  * self.args.gradient_accumulation_steps
                  * self.args.train_batch_size
                  * self.accelerator.num_processes
              )
  
          if self.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
  
          # We start by only optimizing the embeddings
          params_to_optimize = self.text_encoder.get_input_embeddings().parameters()
          optimizer = optimizer_class(
              params_to_optimize,
              lr=self.args.initial_learning_rate,
              betas=(self.args.adam_beta1, self.args.adam_beta2),
              weight_decay=self.args.adam_weight_decay,
              eps=self.args.adam_epsilon,
          )
  
          # Dataset and DataLoaders creation:
          train_dataset = DreamBoothDataset(
              instance_data_root=self.args.instance_data_dir,
              placeholder_tokens=self.placeholder_tokens,
              class_data_root=self.args.class_data_dir
              if self.args.with_prior_preservation
              else None,
              class_prompt=self.args.class_prompt,
              tokenizer=self.tokenizer,
              size=self.args.resolution,
              center_crop=self.args.center_crop,
              num_of_assets=self.args.num_of_assets,
          )
  
          train_dataloader = torch.utils.data.DataLoader(
              train_dataset,
              batch_size=self.args.train_batch_size,
              shuffle=True,
              collate_fn=lambda examples: collate_fn(
                  examples, self.args.with_prior_preservation
              ),
              num_workers=self.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) / self.args.gradient_accumulation_steps
          )
          if self.args.max_train_steps is None:
              self.args.max_train_steps = (
                  self.args.num_train_epochs * num_update_steps_per_epoch
              )
              overrode_max_train_steps = True
  
          lr_scheduler = get_scheduler(
              self.args.lr_scheduler,
              optimizer=optimizer,
              num_warmup_steps=self.args.lr_warmup_steps
              * self.args.gradient_accumulation_steps,
              num_training_steps=self.args.max_train_steps
              * self.args.gradient_accumulation_steps,
              num_cycles=self.args.lr_num_cycles,
              power=self.args.lr_power,
          )
  
          (
              self.unet,
              self.text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = self.accelerator.prepare(
              self.unet, self.text_encoder, optimizer, train_dataloader, lr_scheduler
          )
  
          # For mixed precision training we cast the text_encoder and vae weights to half-precision
          # as these models are only used for inference, keeping weights in full precision is not required.
          self.weight_dtype = torch.float32
          if self.accelerator.mixed_precision == "fp16":
              self.weight_dtype = torch.float16
          elif self.accelerator.mixed_precision == "bf16":
              self.weight_dtype = torch.bfloat16
  
          # Move vae and text_encoder to device and cast to weight_dtype
          self.vae.to(self.accelerator.device, dtype=self.weight_dtype)
  
          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 self.accelerator.unwrap_model(self.unet).dtype != torch.float32:
              raise ValueError(
                  f"Unet loaded as datatype {self.accelerator.unwrap_model(self.unet).dtype}. {low_precision_error_string}"
              )
  
          if (
              self.args.train_text_encoder
              and self.accelerator.unwrap_model(self.text_encoder).dtype != torch.float32
          ):
              raise ValueError(
                  f"Text encoder loaded as datatype {self.accelerator.unwrap_model(self.text_encoder).dtype}."
                  f" {low_precision_error_string}"
              )
  
          # 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) / self.args.gradient_accumulation_steps
          )
          if overrode_max_train_steps:
              self.args.max_train_steps = (
                  self.args.num_train_epochs * num_update_steps_per_epoch
              )
          # Afterwards we recalculate our number of training epochs
          self.args.num_train_epochs = math.ceil(
              self.args.max_train_steps / num_update_steps_per_epoch
          )
  
          if len(self.args.initializer_tokens) > 0:
              # Only for logging
              self.args.initializer_tokens = ", ".join(self.args.initializer_tokens)
  
          # We need to initialize the trackers we use, and also store our configuration.
          # The trackers initializes automatically on the main process.
          if self.accelerator.is_main_process:
              self.accelerator.init_trackers("dreambooth", config=vars(self.args))
  
          # Train
          total_batch_size = (
              self.args.train_batch_size
              * self.accelerator.num_processes
              * self.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 = {self.args.num_train_epochs}")
          logger.info(
              f"  Instantaneous batch size per device = {self.args.train_batch_size}"
          )
          logger.info(
              f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
          )
          logger.info(
              f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps}"
          )
          logger.info(f"  Total optimization steps = {self.args.max_train_steps}")
          global_step = 0
          first_epoch = 0
  
          # Potentially load in the weights and states from a previous save
          if self.args.resume_from_checkpoint:
              if self.args.resume_from_checkpoint != "latest":
                  path = os.path.basename(self.args.resume_from_checkpoint)
              else:
                  # Get the mos recent checkpoint
                  dirs = os.listdir(self.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:
                  self.accelerator.print(
                      f"Checkpoint '{self.args.resume_from_checkpoint}' does not exist. Starting a new training run."
                  )
                  self.args.resume_from_checkpoint = None
              else:
                  self.accelerator.print(f"Resuming from checkpoint {path}")
                  self.accelerator.load_state(os.path.join(self.args.output_dir, path))
                  global_step = int(path.split("-")[1])
  
                  resume_global_step = global_step * self.args.gradient_accumulation_steps
                  first_epoch = global_step // num_update_steps_per_epoch
                  resume_step = resume_global_step % (
                      num_update_steps_per_epoch * self.args.gradient_accumulation_steps
                  )
  
          # Only show the progress bar once on each machine.
          progress_bar = tqdm(
              range(global_step, self.args.max_train_steps),
              disable=not self.accelerator.is_local_main_process,
          )
          progress_bar.set_description("Steps")
  
          # keep original embeddings as reference
          orig_embeds_params = (
              self.accelerator.unwrap_model(self.text_encoder)
              .get_input_embeddings()
              .weight.data.clone()
          )
  
          # Create attention controller
          self.controller = AttentionStore()
          self.register_attention_control(self.controller)
  
          for epoch in range(first_epoch, self.args.num_train_epochs):
              self.unet.train()
              if self.args.train_text_encoder:
                  self.text_encoder.train()
              for step, batch in enumerate(train_dataloader):
                  if self.args.phase1_train_steps == global_step:
                      self.unet.requires_grad_(True)
                      if self.args.train_text_encoder:
                          self.text_encoder.requires_grad_(True)
                      unet_params = self.unet.parameters()
  
                      params_to_optimize = (
                          itertools.chain(unet_params, self.text_encoder.parameters())
                          if self.args.train_text_encoder
                          else itertools.chain(
                              unet_params,
                              self.text_encoder.get_input_embeddings().parameters(),
                          )
                      )
                      del optimizer
                      optimizer = optimizer_class(
                          params_to_optimize,
                          lr=self.args.learning_rate,
                          betas=(self.args.adam_beta1, self.args.adam_beta2),
                          weight_decay=self.args.adam_weight_decay,
                          eps=self.args.adam_epsilon,
                      )
                      del lr_scheduler
                      lr_scheduler = get_scheduler(
                          self.args.lr_scheduler,
                          optimizer=optimizer,
                          num_warmup_steps=self.args.lr_warmup_steps
                          * self.args.gradient_accumulation_steps,
                          num_training_steps=self.args.max_train_steps
                          * self.args.gradient_accumulation_steps,
                          num_cycles=self.args.lr_num_cycles,
                          power=self.args.lr_power,
                      )
                      optimizer, lr_scheduler = self.accelerator.prepare(
                          optimizer, lr_scheduler
                      )
  
                  logs = {}
  
                  # Skip steps until we reach the resumed step
                  if (
                      self.args.resume_from_checkpoint
                      and epoch == first_epoch
                      and step < resume_step
                  ):
                      if step % self.args.gradient_accumulation_steps == 0:
                          progress_bar.update(1)
                      continue
  
                  with self.accelerator.accumulate(self.unet):
                      # Convert images to latent space
                      latents = self.vae.encode(
                          batch["pixel_values"].to(dtype=self.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,
                          self.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 = self.noise_scheduler.add_noise(
                          latents, noise, timesteps
                      )
  
                      # Get the text embedding for conditioning
                      encoder_hidden_states = self.text_encoder(batch["input_ids"])[0]
  
                      # Predict the noise residual
                      model_pred = self.unet(
                          noisy_latents, timesteps, encoder_hidden_states
                      ).sample
  
                      # Get the target for loss depending on the prediction type
                      if self.noise_scheduler.config.prediction_type == "epsilon":
                          target = noise
                      elif self.noise_scheduler.config.prediction_type == "v_prediction":
                          target = self.noise_scheduler.get_velocity(
                              latents, noise, timesteps
                          )
                      else:
                          raise ValueError(
                              f"Unknown prediction type {self.noise_scheduler.config.prediction_type}"
                          )
  
                      if self.args.with_prior_preservation:
                          # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                          model_pred_prior, model_pred = torch.chunk(model_pred, 2, dim=0)
                          target_prior, target = torch.chunk(target, 2, dim=0)
  
                          if self.args.apply_masked_loss:
                              max_masks = torch.max(
                                  batch["instance_masks"], axis=1
                              ).values
                              downsampled_mask = F.interpolate(
                                  input=max_masks, size=(64, 64)
                              )
                              model_pred = model_pred * downsampled_mask
                              target = target * downsampled_mask
  
                          # Compute instance loss
                          loss = F.mse_loss(
                              model_pred.float(), target.float(), reduction="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 + self.args.prior_loss_weight * prior_loss
                      else:
                          if self.args.apply_masked_loss:
                              max_masks = torch.max(
                                  batch["instance_masks"], axis=1
                              ).values
                              downsampled_mask = F.interpolate(
                                  input=max_masks, size=(64, 64)
                              )
                              model_pred = model_pred * downsampled_mask
                              target = target * downsampled_mask
                          loss = F.mse_loss(
                              model_pred.float(), target.float(), reduction="mean"
                          )
  
                      # Attention loss
                      if self.args.lambda_attention != 0:
                          attn_loss = 0
                          for batch_idx in range(self.args.train_batch_size):
                              GT_masks = F.interpolate(
                                  input=batch["instance_masks"][batch_idx], size=(16, 16)
                              )
                              agg_attn = self.aggregate_attention(
                                  res=16,
                                  from_where=("up", "down"),
                                  is_cross=True,
                                  select=batch_idx,
                              )
                              curr_cond_batch_idx = self.args.train_batch_size + batch_idx
  
                              for mask_id in range(len(GT_masks)):
                                  curr_placeholder_token_id = self.placeholder_token_ids[
                                      batch["token_ids"][batch_idx][mask_id]
                                  ]
  
                                  asset_idx = (
                                      (
                                          batch["input_ids"][curr_cond_batch_idx]
                                          == curr_placeholder_token_id
                                      )
                                      .nonzero()
                                      .item()
                                  )
                                  asset_attn_mask = agg_attn[..., asset_idx]
                                  asset_attn_mask = (
                                      asset_attn_mask / asset_attn_mask.max()
                                  )
                                  attn_loss += F.mse_loss(
                                      GT_masks[mask_id, 0].float(),
                                      asset_attn_mask.float(),
                                      reduction="mean",
                                  )
  
                          attn_loss = self.args.lambda_attention * (
                              attn_loss / self.args.train_batch_size
                          )
                          logs["attn_loss"] = attn_loss.detach().item()
                          loss += attn_loss
  
                      self.accelerator.backward(loss)
  
                      # No need to keep the attention store
                      self.controller.attention_store = {}
                      self.controller.cur_step = 0
  
                      if self.accelerator.sync_gradients:
                          params_to_clip = (
                              itertools.chain(
                                  self.unet.parameters(), self.text_encoder.parameters()
                              )
                              if self.args.train_text_encoder
                              else self.unet.parameters()
                          )
                          self.accelerator.clip_grad_norm_(
                              params_to_clip, self.args.max_grad_norm
                          )
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad(set_to_none=self.args.set_grads_to_none)
  
                      if global_step < self.args.phase1_train_steps:
                          # Let's make sure we don't update any embedding weights besides the newly added token
                          with torch.no_grad():
                              self.accelerator.unwrap_model(
                                  self.text_encoder
                              ).get_input_embeddings().weight[
                                  : -self.args.num_of_assets
                              ] = orig_embeds_params[
                                  : -self.args.num_of_assets
                              ]
  
                  # Checks if the accelerator has performed an optimization step behind the scenes
                  if self.accelerator.sync_gradients:
                      progress_bar.update(1)
                      global_step += 1
  
                      if global_step % self.args.checkpointing_steps == 0:
                          if self.accelerator.is_main_process:
                              save_path = os.path.join(
                                  self.args.output_dir, f"checkpoint-{global_step}"
                              )
                              self.accelerator.save_state(save_path)
                              logger.info(f"Saved state to {save_path}")
  
                      if (
                          self.args.log_checkpoints
                          and global_step % self.args.img_log_steps == 0
                          and global_step > self.args.phase1_train_steps
                      ):
                          ckpts_path = os.path.join(
                              self.args.output_dir, "checkpoints", f"{global_step:05}"
                          )
                          os.makedirs(ckpts_path, exist_ok=True)
                          self.save_pipeline(ckpts_path)
  
                          img_logs_path = os.path.join(self.args.output_dir, "img_logs")
                          os.makedirs(img_logs_path, exist_ok=True)
  
                          if self.args.lambda_attention != 0:
                              self.controller.cur_step = 1
                              last_sentence = batch["input_ids"][curr_cond_batch_idx]
                              last_sentence = last_sentence[
                                  (last_sentence != 0)
                                  & (last_sentence != 49406)
                                  & (last_sentence != 49407)
                              ]
                              last_sentence = self.tokenizer.decode(last_sentence)
                              self.save_cross_attention_vis(
                                  last_sentence,
                                  attention_maps=agg_attn.detach().cpu(),
                                  path=os.path.join(
                                      img_logs_path, f"{global_step:05}_step_attn.jpg"
                                  ),
                              )
                          self.controller.cur_step = 0
                          self.controller.attention_store = {}
  
                          self.perform_full_inference(
                              path=os.path.join(
                                  img_logs_path, f"{global_step:05}_full_pred.jpg"
                              )
                          )
                          full_agg_attn = self.aggregate_attention(
                              res=16, from_where=("up", "down"), is_cross=True, select=0
                          )
                          self.save_cross_attention_vis(
                              self.args.instance_prompt,
                              attention_maps=full_agg_attn.detach().cpu(),
                              path=os.path.join(
                                  img_logs_path, f"{global_step:05}_full_attn.jpg"
                              ),
                          )
                          self.controller.cur_step = 0
                          self.controller.attention_store = {}
  
                  logs["loss"] = loss.detach().item()
                  logs["lr"] = lr_scheduler.get_last_lr()[0]
                  progress_bar.set_postfix(**logs)
                  self.accelerator.log(logs, step=global_step)
  
                  if global_step >= self.args.max_train_steps:
                      break
  
          self.save_pipeline(self.args.output_dir)
  
          self.accelerator.end_training()
  
      def save_pipeline(self, path):
          self.accelerator.wait_for_everyone()
          if self.accelerator.is_main_process:
              pipeline = DiffusionPipeline.from_pretrained(
                  self.args.pretrained_model_name_or_path,
                  unet=self.accelerator.unwrap_model(self.unet),
                  text_encoder=self.accelerator.unwrap_model(self.text_encoder),
                  tokenizer=self.tokenizer,
                  revision=self.args.revision,
              )
              pipeline.save_pretrained(path)
  
      def register_attention_control(self, controller):
          attn_procs = {}
          cross_att_count = 0
          for name in self.unet.attn_processors.keys():
              cross_attention_dim = (
                  None
                  if name.endswith("attn1.processor")
                  else self.unet.config.cross_attention_dim
              )
              if name.startswith("mid_block"):
                  hidden_size = self.unet.config.block_out_channels[-1]
                  place_in_unet = "mid"
              elif name.startswith("up_blocks"):
                  block_id = int(name[len("up_blocks.")])
                  hidden_size = list(reversed(self.unet.config.block_out_channels))[
                      block_id
                  ]
                  place_in_unet = "up"
              elif name.startswith("down_blocks"):
                  block_id = int(name[len("down_blocks.")])
                  hidden_size = self.unet.config.block_out_channels[block_id]
                  place_in_unet = "down"
              else:
                  continue
              cross_att_count += 1
              attn_procs[name] = P2PCrossAttnProcessor(
                  controller=controller, place_in_unet=place_in_unet
              )
  
          self.unet.set_attn_processor(attn_procs)
          controller.num_att_layers = cross_att_count
  
      def get_average_attention(self):
          average_attention = {
              key: [
                  item / self.controller.cur_step
                  for item in self.controller.attention_store[key]
              ]
              for key in self.controller.attention_store
          }
          return average_attention
  
      def aggregate_attention(
          self, res: int, from_where: List[str], is_cross: bool, select: int
      ):
          out = []
          attention_maps = self.get_average_attention()
          num_pixels = res**2
          for location in from_where:
              for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
                  if item.shape[1] == num_pixels:
                      cross_maps = item.reshape(
                          self.args.train_batch_size, -1, res, res, item.shape[-1]
                      )[select]
                      out.append(cross_maps)
          out = torch.cat(out, dim=0)
          out = out.sum(0) / out.shape[0]
          return out
  
      @torch.no_grad()
      def perform_full_inference(self, path, guidance_scale=7.5):
          self.unet.eval()
          self.text_encoder.eval()
  
          latents = torch.randn((1, 4, 64, 64), device=self.accelerator.device)
          uncond_input = self.tokenizer(
              [""],
              padding="max_length",
              max_length=self.tokenizer.model_max_length,
              return_tensors="pt",
          ).to(self.accelerator.device)
          input_ids = self.tokenizer(
              [self.args.instance_prompt],
              padding="max_length",
              truncation=True,
              max_length=self.tokenizer.model_max_length,
              return_tensors="pt",
          ).input_ids.to(self.accelerator.device)
          cond_embeddings = self.text_encoder(input_ids)[0]
          uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
          text_embeddings = torch.cat([uncond_embeddings, cond_embeddings])
  
          for t in self.validation_scheduler.timesteps:
              latent_model_input = torch.cat([latents] * 2)
              latent_model_input = self.validation_scheduler.scale_model_input(
                  latent_model_input, timestep=t
              )
  
              pred = self.unet(
                  latent_model_input, t, encoder_hidden_states=text_embeddings
              )
              noise_pred = pred.sample
  
              noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
              noise_pred = noise_pred_uncond + guidance_scale * (
                  noise_pred_text - noise_pred_uncond
              )
  
              latents = self.validation_scheduler.step(noise_pred, t, latents).prev_sample
          latents = 1 / 0.18215 * latents
  
          images = self.vae.decode(latents.to(self.weight_dtype)).sample
          images = (images / 2 + 0.5).clamp(0, 1)
          images = images.detach().cpu().permute(0, 2, 3, 1).numpy()
          images = (images * 255).round().astype("uint8")
  
          self.unet.train()
          if self.args.train_text_encoder:
              self.text_encoder.train()
  
          Image.fromarray(images[0]).save(path)
  
      @torch.no_grad()
      def save_cross_attention_vis(self, prompt, attention_maps, path):
          tokens = self.tokenizer.encode(prompt)
          images = []
          for i in range(len(tokens)):
              image = attention_maps[:, :, i]
              image = 255 * image / image.max()
              image = image.unsqueeze(-1).expand(*image.shape, 3)
              image = image.numpy().astype(np.uint8)
              image = np.array(Image.fromarray(image).resize((256, 256)))
              image = ptp_utils.text_under_image(
                  image, self.tokenizer.decode(int(tokens[i]))
              )
              images.append(image)
          vis = ptp_utils.view_images(np.stack(images, axis=0))
          vis.save(path)
  
  class P2PCrossAttnProcessor:
      def __init__(self, controller, place_in_unet):
          super().__init__()
          self.controller = controller
          self.place_in_unet = place_in_unet
  
      def __call__(
          self,
          attn: CrossAttention,
          hidden_states,
          encoder_hidden_states=None,
          attention_mask=None,
      ):
          batch_size, sequence_length, _ = hidden_states.shape
          attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
  
          query = attn.to_q(hidden_states)
  
          is_cross = encoder_hidden_states is not None
          encoder_hidden_states = (
              encoder_hidden_states
              if encoder_hidden_states is not None
              else hidden_states
          )
          key = attn.to_k(encoder_hidden_states)
          value = attn.to_v(encoder_hidden_states)
  
          query = attn.head_to_batch_dim(query)
          key = attn.head_to_batch_dim(key)
          value = attn.head_to_batch_dim(value)
  
          attention_probs = attn.get_attention_scores(query, key, attention_mask)
  
          # one line change
          self.controller(attention_probs, is_cross, self.place_in_unet)
  
          hidden_states = torch.bmm(attention_probs, value)
          hidden_states = attn.batch_to_head_dim(hidden_states)
  
          # linear proj
          hidden_states = attn.to_out[0](hidden_states)
          # dropout
          hidden_states = attn.to_out[1](hidden_states)
  
          return hidden_states
  
  
  if __name__ == "__main__":
      SpatialDreambooth()
  