concept-ablation-diffusers/train.py

# This code is modified from the Huggingface repository: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py, and
import argparse
import hashlib
import itertools
import json
import logging
import math
import os
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 HfApi, create_repo
from model_pipeline import (
 CustomDiffusionAttnProcessor,
 CustomDiffusionPipeline,
 set_use_memory_efficient_attention_xformers,
)
from packaging import version
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from utils import (
 CustomDiffusionDataset,
 PromptDataset,
 collate_fn,
 filter,
 getanchorprompts,
)

import diffusers
from diffusers import (
 AutoencoderKL,
 DDPMScheduler,
 DiffusionPipeline,
 DPMSolverMultistepScheduler,
 UNet2DConditionModel,
)
from diffusers.models.cross_attention import CrossAttention
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.14.0")

logger = get_logger(__name__)


def create_custom_diffusion(unet, parameter_group):
 for name, params in unet.named_parameters():
 if parameter_group == "cross-attn":
 if 'attn2.to_k' in name or 'attn2.to_v' in name:
 params.requires_grad = True
 else:
 params.requires_grad = False
 elif parameter_group == 'full-weight':
 params.requires_grad = True
 elif parameter_group == 'embedding':
 params.requires_grad = False
 else:
 raise ValueError(
 "parameter_group argument only cross-attn, full-weight, embedding"
 )

 # change attn class
 def change_attn(unet):
 for layer in unet.children():
 if type(layer) == CrossAttention:
 bound_method = set_use_memory_efficient_attention_xformers.__get__(
 layer, layer.__class__)
 setattr(
 layer, 'set_use_memory_efficient_attention_xformers', bound_method)
 else:
 change_attn(layer)

 change_attn(unet)
 unet.set_attn_processor(CustomDiffusionAttnProcessor())
 return unet


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

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

 These are Custom Diffusion adaption weights for {base_model}. The weights were trained on {prompt} using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. \n
 {img_str[0]}
 """
 with open(os.path.join(repo_folder, "README.md"), "w") as f:
 f.write(yaml + model_card)


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 freeze_params(params):
 for param in params:
 param.requires_grad = False


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(
 "--tokenizer_name",
 type=str,
 default=None,
 help="Pretrained tokenizer name or path if not the same as model_name",
 )
 parser.add_argument(
 "--concept_type",
 type=str,
 required=True,
 choices=['style', 'object', 'memorization'],
 help='the type of removed concepts'
 )
 parser.add_argument(
 "--caption_target",
 type=str,
 required=True,
 help="target style to remove, used when kldiv loss",
 )
 parser.add_argument(
 "--instance_data_dir",
 type=str,
 default=None,
 help="A folder containing the training data of instance images.",
 )
 parser.add_argument(
 "--class_data_dir",
 type=str,
 default=None,
 help="A folder containing the training data of class images.",
 )
 parser.add_argument(
 "--instance_prompt",
 type=str,
 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(
 "--mem_impath",
 type=str,
 default="",
 help='the path to saved memorized image. Required when concept_type is memorization'
 )
 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=2,
 help="Number of images that should be generated during validation with `validation_prompt`.",
 )
 parser.add_argument(
 "--validation_steps",
 type=int,
 default=500,
 help=(
 "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
 " `args.validation_prompt` multiple times: `args.num_validation_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(
 "--train_size",
 type=int,
 default=1000,
 help='the number of generated images used for ablating the concept'
 )
 parser.add_argument(
 "--output_dir",
 type=str,
 default="custom-diffusion-model",
 help="The output directory where the model predictions and checkpoints will be written.",
 )
 parser.add_argument(
 "--num_class_images",
 type=int,
 default=1000,
 help=(
 "Minimal anchor class images. If there are not enough images already present in"
 " class_data_dir, additional images will be sampled with class_prompt."
 ),
 )
 parser.add_argument(
 "--num_class_prompts",
 type=int,
 default=200,
 help=(
 "Minimal prompts used to generate anchor class images"
 ),
 )
 parser.add_argument("--seed", type=int, default=42,
 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_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=250,
 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(
 "--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 docs"
 ),
 )
 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=1e-5,
 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(
 "--dataloader_num_workers",
 type=int,
 default=2,
 help=(
 "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
 ),
 )
 parser.add_argument(
 "--parameter_group",
 type=str,
 default='cross-attn',
 choices=['full-weight', 'cross-attn', 'embedding'],
 help='parameter groups to finetune. Default: full-weight for memorization and cross-attn for others'
 )
 parser.add_argument(
 "--loss_type_reverse",
 type=str,
 default='model-based',
 help="loss type for reverse fine-tuning",
 )
 parser.add_argument(
 "--lr_scheduler",
 type=str,
 default="constant",
 help=(
 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
 ' "constant", "constant_with_warmup"]'
 ),
 )
 parser.add_argument(
 "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
 )
 parser.add_argument(
 "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
 )
 parser.add_argument("--adam_beta1", type=float, default=0.9,
 help="The beta1 parameter for the Adam optimizer.")
 parser.add_argument("--adam_beta2", type=float, default=0.999,
 help="The beta2 parameter for the Adam optimizer.")
 parser.add_argument("--adam_weight_decay", type=float,
 default=1e-2, help="Weight decay to use.")
 parser.add_argument("--adam_epsilon", type=float, default=1e-08,
 help="Epsilon value for the Adam optimizer")
 parser.add_argument("--max_grad_norm", default=1.0,
 type=float, help="Max gradient norm.")
 parser.add_argument("--push_to_hub", action="store_true",
 help="Whether or not to push the model to the Hub.")
 parser.add_argument("--hub_token", type=str, default=None,
 help="The token to use to push to the Model Hub.")
 parser.add_argument(
 "--hub_model_id",
 type=str,
 default=None,
 help="The name of the repository to keep in sync with the local `output_dir`.",
 )
 parser.add_argument(
 "--logging_dir",
 type=str,
 default="logs",
 help=(
 "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
 " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
 ),
 )
 parser.add_argument(
 "--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=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(
 "--concepts_list",
 type=str,
 default=None,
 help="Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.",
 )
 parser.add_argument(
 "--openai_key",
 type=str,
 default="",
 help=(
 "OPENAI API key. required for ablating objects and memorized images."
 ),
 )
 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("--hflip", action="store_true",
 help="Apply horizontal flip data augmentation.")
 parser.add_argument("--noaug", action="store_true",
 help="Dont apply augmentation during data augmentation when this flag is enabled.")

 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.concepts_list is None:
 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


def main(args):
 logging_dir = Path(args.output_dir, args.logging_dir)

 accelerator_project_config = ProjectConfiguration(
 total_limit=args.checkpoints_total_limit)

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

 if args.report_to == "wandb":
 if not is_wandb_available():
 raise ImportError(
 "Make sure to install wandb if you want to use it for logging during training.")
 import wandb

 # 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.
 # 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()

 # 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:
 print(vars(args))
 accelerator.init_trackers("custom-diffusion", config=vars(args))

 # If passed along, set the training seed now.
 if args.seed is not None:
 set_seed(args.seed)
 if args.concepts_list is None:
 args.concepts_list = [
 {
 "instance_prompt": args.instance_prompt,
 "class_prompt": args.class_prompt,
 "instance_data_dir": args.instance_data_dir,
 "class_data_dir": args.class_data_dir,
 "caption_target": args.caption_target,
 }
 ]
 else:
 with open(args.concepts_list, "r") as f:
 args.concepts_list = json.load(f)

 # Generate class images if prior preservation is enabled.
 for i, concept in enumerate(args.concepts_list):
 # directly path to ablation images and its corresponding prompts is provided.
 if (concept['instance_prompt'] is not None and concept['instance_data_dir'] is not None):
 break

 class_images_dir = Path(concept['class_data_dir'])
 if not class_images_dir.exists():
 class_images_dir.mkdir(parents=True, exist_ok=True)
 os.makedirs(f'{class_images_dir}/images', exist_ok=True)

 # we need to generate training images
 if len(list(Path(os.path.join(class_images_dir, 'images')).iterdir())) < 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,
 )
 pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
 pipeline.scheduler.config)

 pipeline.set_progress_bar_config(disable=True)
 pipeline.to(accelerator.device)

 # need to create prompts using class_prompt.
 if not os.path.isfile(concept['class_prompt']):
 # style based prompts are retrieved from laion dataset
 if args.concept_type == 'style':
 with open(os.path.join(class_images_dir, 'painting.txt')) as f:
 class_prompt_collection = [
 x.strip() for x in f.readlines()]

 # LLM based prompt collection.
 else:
 class_prompt = concept['class_prompt']
 # in case of object query chatGPT to generate captions containing the anchor category
 if args.concept_type == 'object':
 class_prompt_collection, _ = getanchorprompts(
 pipeline, accelerator, class_prompt, args.concept_type, class_images_dir, args.openai_key, args.num_class_prompts)
 with open(class_images_dir / 'caption_anchor.txt', 'w') as f:
 for prompt in class_prompt_collection:
 f.write(prompt + '\n')
 # in case of memorization query chatGPT to generate different captions that can be paraphrase of the origianl caption
 elif args.concept_type == 'memorization':
 class_prompt_collection, caption_target = getanchorprompts(
 pipeline, accelerator, class_prompt, args.concept_type, class_images_dir, args.openai_key, args.num_class_prompts, mem_impath=args.mem_impath)
 concept['caption_target'] += f';*+{caption_target}'
 with open(class_images_dir / 'caption_target.txt', 'w') as f:
 f.write(concept['caption_target'])
 print(class_prompt_collection,
 concept['caption_target'])
 # class_prompt is filepath to prompts.
 else:
 with open(concept['class_prompt']) as f:
 class_prompt_collection = [
 x.strip() for x in f.readlines()]

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

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

 sample_dataloader = accelerator.prepare(sample_dataloader)

 if os.path.exists(f'{class_images_dir}/caption.txt'):
 os.remove(f'{class_images_dir}/caption.txt')
 if os.path.exists(f'{class_images_dir}/images.txt'):
 os.remove(f'{class_images_dir}/images.txt')

 for example in tqdm(
 sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
 ):
 accelerator.wait_for_everyone()
 with open(f'{class_images_dir}/caption.txt', 'a') as f1, open(f'{class_images_dir}/images.txt', 'a') as f2:
 images = pipeline(example["prompt"], num_inference_steps=25, guidance_scale=6., eta=1.).images

 for i, image in enumerate(images):
 hash_image = hashlib.sha1(
 image.tobytes()).hexdigest()
 image_filename = class_images_dir / \
 f"images/{example['index'][i]}-{hash_image}.jpg"
 image.save(image_filename)
 f2.write(str(image_filename)+'\n')
 f1.write('\n'.join(example["prompt"]) + '\n')
 accelerator.wait_for_everyone()

 del pipeline

 if args.concept_type == 'memorization':
 filter(class_images_dir, args.mem_impath,
 outpath=str(class_images_dir / 'filtered'))
 if os.path.exists(class_images_dir / 'caption_target.txt'):
 with open(class_images_dir / 'caption_target.txt', 'r') as f:
 concept['caption_target'] = f.readlines()[0].strip()
 class_images_dir = class_images_dir / 'filtered'

 concept['class_prompt'] = os.path.join(
 class_images_dir, 'caption.txt')
 concept['class_data_dir'] = os.path.join(
 class_images_dir, 'images.txt')
 concept['instance_prompt'] = os.path.join(
 class_images_dir, 'caption.txt')
 concept['instance_data_dir'] = os.path.join(
 class_images_dir, 'images.txt')

 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:
 print(args.hub_model_id or Path(args.output_dir).name)
 repo_id = create_repo(
 repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
 )
 print(repo_id)
 repo_id = args.hub_model_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
 )
 vae = AutoencoderKL.from_pretrained(
 args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
 unet = UNet2DConditionModel.from_pretrained(
 args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
 )

 vae.requires_grad_(False)
 if args.parameter_group != 'embedding':
 text_encoder.requires_grad_(False)
 unet = create_custom_diffusion(unet, args.parameter_group)

 # For mixed precision training we cast the text_encoder and vae weights to half-precision
 # as these models are only used for inference, keeping weights in full precision is not required.
 weight_dtype = torch.float32
 if accelerator.mixed_precision == "fp16":
 weight_dtype = torch.float16
 elif accelerator.mixed_precision == "bf16":
 weight_dtype = torch.bfloat16

 # Move unet, vae and text_encoder to device and cast to weight_dtype
 if accelerator.mixed_precision != "fp16":
 unet.to(accelerator.device, dtype=weight_dtype)
 text_encoder.to(accelerator.device, dtype=weight_dtype)
 vae.to(accelerator.device, dtype=weight_dtype)

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

 if args.gradient_checkpointing:
 unet.enable_gradient_checkpointing()
 if args.parameter_group == 'embedding':
 text_encoder.gradient_checkpointing_enable()
 # 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
 )
 if args.with_prior_preservation:
 args.learning_rate = args.learning_rate * 2.

 # 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

 # Adding a modifier token which is optimized ####
 # Code taken from https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
 modifier_token_id = []
 if args.parameter_group == 'embedding':
 assert args.concept_type != 'memorization', "embedding finetuning is not supported for memorization"

 for concept in args.concept_list:
 # Convert the caption_target to ids
 token_ids = tokenizer.encode(
 [concept['caption_target']], add_special_tokens=False)
 print(token_ids)
 # Check if initializer_token is a single token or a sequence of tokens
 modifier_token_id += token_ids

 # Freeze all parameters except for the token embeddings in text encoder
 params_to_freeze = itertools.chain(
 text_encoder.text_model.encoder.parameters(),
 text_encoder.text_model.final_layer_norm.parameters(),
 text_encoder.text_model.embeddings.position_embedding.parameters(),
 )
 freeze_params(params_to_freeze)
 params_to_optimize = itertools.chain(
 text_encoder.get_input_embeddings().parameters())
 else:
 if args.parameter_group == 'cross-attn':
 params_to_optimize = itertools.chain([x[1] for x in unet.named_parameters() if (
 'attn2.to_k' in x[0] or 'attn2.to_v' in x[0])])
 if args.parameter_group == 'full-weight':
 params_to_optimize = itertools.chain(unet.parameters())

 # Optimizer creation
 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,
 )

 # Dataset and DataLoaders creation:
 train_dataset = CustomDiffusionDataset(
 concepts_list=args.concepts_list,
 concept_type=args.concept_type,
 tokenizer=tokenizer,
 with_prior_preservation=args.with_prior_preservation,
 size=args.resolution,
 center_crop=args.center_crop,
 num_class_images=args.num_class_images,
 hflip=args.hflip, aug=not args.noaug,
 )

 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 * args.gradient_accumulation_steps,
 num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
 )

 # Prepare everything with our `accelerator`.
 if args.parameter_group == 'embedding':
 text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
 text_encoder, optimizer, train_dataloader, lr_scheduler
 )
 else:
 unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
 unet, optimizer, train_dataloader, lr_scheduler
 )

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

 # 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 mos recent checkpoint
 dirs = os.listdir(args.output_dir)
 dirs = [d for d in dirs if d.startswith("checkpoint")]
 dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
 path = dirs[-1] if len(dirs) > 0 else None

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

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

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

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

 with accelerator.accumulate(unet) if args.parameter_group != 'embedding' else accelerator.accumulate(text_encoder):
 # Convert images to latent space
 latents = vae.encode(batch["pixel_values"].to(
 dtype=weight_dtype)).latent_dist.sample()
 latents = latents * vae.config.scaling_factor

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

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

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

 # Predict the noise residual
 model_pred = unet(noisy_latents, timesteps,
 encoder_hidden_states).sample
 with torch.no_grad():
 model_pred_anchor = unet(noisy_latents[:encoder_anchor_hidden_states.size(
 0)], timesteps[:encoder_anchor_hidden_states.size(0)], encoder_anchor_hidden_states).sample

 # Get the target for loss depending on the prediction type
 if args.loss_type_reverse == 'model-based':
 if args.with_prior_preservation:
 target_prior = torch.chunk(noise, 2, dim=0)[1]
 target = model_pred_anchor
 else:
 if noise_scheduler.config.prediction_type == "epsilon":
 target = noise
 elif noise_scheduler.config.prediction_type == "v_prediction":
 target = noise_scheduler.get_velocity(
 latents, noise, timesteps)
 else:
 raise ValueError(
 f"Unknown prediction type {noise_scheduler.config.prediction_type}")
 if args.with_prior_preservation:
 target, target_prior = torch.chunk(target, 2, dim=0)

 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)
 mask = torch.chunk(batch["mask"], 2, dim=0)[0]
 # Compute instance loss
 loss = F.mse_loss(model_pred.float(),
 target.float(), reduction="none")
 loss = (
 (loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean()

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

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

 accelerator.backward(loss)
 # Zero out the gradients for all token embeddings except the newly added
 # embeddings for the concept, as we only want to optimize the concept embeddings
 if args.parameter_group == 'embedding':
 if accelerator.num_processes > 1:
 grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad
 else:
 grads_text_encoder = text_encoder.get_input_embeddings().weight.grad
 # Get the index for tokens that we want to zero the grads for
 index_grads_to_zero = torch.arange(
 len(tokenizer)) != modifier_token_id[0]
 for i in range(len(modifier_token_id[1:])):
 index_grads_to_zero = index_grads_to_zero & (
 torch.arange(len(tokenizer)) != modifier_token_id[i])
 grads_text_encoder.data[index_grads_to_zero,
 :] = grads_text_encoder.data[index_grads_to_zero, :].fill_(0)

 if accelerator.sync_gradients:
 params_to_clip = (
 itertools.chain(text_encoder.parameters())
 if args.parameter_group == 'embedding'
 else itertools.chain([x[1] for x in unet.named_parameters() if ('attn2' in x[0])])
 if args.parameter_group == 'cross-attn'
 else itertools.chain(unet.parameters())
 )
 accelerator.clip_grad_norm_(
 params_to_clip, args.max_grad_norm)
 optimizer.step()
 lr_scheduler.step()
 optimizer.zero_grad()

 # Checks if the accelerator has performed an optimization step behind the scenes
 if accelerator.sync_gradients:
 progress_bar.update(1)
 global_step += 1

 if global_step % args.checkpointing_steps == 0:
 if accelerator.is_main_process:
 pipeline = CustomDiffusionPipeline.from_pretrained(
 args.pretrained_model_name_or_path,
 unet=accelerator.unwrap_model(unet),
 text_encoder=accelerator.unwrap_model(
 text_encoder),
 tokenizer=tokenizer,
 revision=args.revision,
 modifier_token_id=modifier_token_id,
 )
 save_path = os.path.join(
 args.output_dir, f"delta-{global_step}")
 pipeline.save_pretrained(
 save_path, parameter_group=args.parameter_group)
 logger.info(f"Saved state to {save_path}")

 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

 if accelerator.is_main_process:
 if args.validation_prompt is not None and global_step % args.validation_steps == 0:
 logger.info(
 f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
 f" {args.validation_prompt}."
 )
 # create pipeline
 pipeline = CustomDiffusionPipeline.from_pretrained(
 args.pretrained_model_name_or_path,
 unet=accelerator.unwrap_model(unet),
 text_encoder=accelerator.unwrap_model(text_encoder),
 tokenizer=tokenizer,
 revision=args.revision,
 modifier_token_id=modifier_token_id,
 )
 pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
 pipeline.scheduler.config)
 pipeline = pipeline.to(accelerator.device)
 pipeline.set_progress_bar_config(disable=True)

 # run inference
 generator = torch.Generator(
 device=accelerator.device).manual_seed(args.seed)
 images = [
 pipeline(args.validation_prompt, num_inference_steps=25,
 generator=generator, eta=1.).images[0]
 for _ in range(args.num_validation_images)
 ]

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

 del pipeline
 torch.cuda.empty_cache()

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
 unet = unet.to(torch.float32)
 pipeline = CustomDiffusionPipeline.from_pretrained(
 args.pretrained_model_name_or_path,
 unet=accelerator.unwrap_model(unet),
 text_encoder=accelerator.unwrap_model(text_encoder),
 tokenizer=tokenizer,
 revision=args.revision,
 modifier_token_id=modifier_token_id,
 )
 save_path = os.path.join(args.output_dir, "delta.bin")
 pipeline.save_pretrained(
 save_path, parameter_group=args.parameter_group)

 # run inference
 if args.validation_prompt and args.num_validation_images > 0:
 pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
 pipeline.scheduler.config)
 pipeline = pipeline.to(accelerator.device)
 pipeline.set_progress_bar_config(disable=True)

 # run inference
 generator = torch.Generator(
 device=accelerator.device).manual_seed(args.seed)
 images = [
 pipeline(args.validation_prompt, num_inference_steps=25,
 generator=generator, eta=1.).images[0]
 for _ in range(args.num_validation_images)
 ]

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

 if args.push_to_hub:
 save_model_card(
 repo_id,
 images=images,
 base_model=args.pretrained_model_name_or_path,
 prompt=args.instance_prompt,
 repo_folder=args.output_dir,
 )
 api = HfApi(token=args.hub_token)
 api.upload_folder(
 repo_id=repo_id,
 folder_path=args.output_dir,
 path_in_repo='.',
 repo_type='model'
 )

 accelerator.end_training()


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