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train-dreambooth-lora-sdxl / train_dreambooth_lora_sdxl.py

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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and

	import argparse
	import gc
	import hashlib
	import itertools
	import logging
	import math
	import os
	import shutil
	import warnings
	from pathlib import Path
	from typing import Dict

	import numpy as np
	import torch
	import torch.nn.functional as F
	import torch.utils.checkpoint
	import transformers
	from accelerate import Accelerator
	from accelerate.logging import get_logger
	from accelerate.utils import ProjectConfiguration, set_seed
	from huggingface_hub import create_repo, upload_folder
	from packaging import version
	from PIL import Image
	from PIL.ImageOps import exif_transpose
	from torch.utils.data import Dataset
	from torchvision import transforms
	from tqdm.auto import tqdm
	from transformers import AutoTokenizer, PretrainedConfig

	import diffusers
	from diffusers import (
	AutoencoderKL,
	DDPMScheduler,
	DPMSolverMultistepScheduler,
	StableDiffusionXLPipeline,
	UNet2DConditionModel,
	)
	from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
	from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0
	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("0.22.0.dev0")

	logger = get_logger(__name__)

	def save_tempo_model_card(
	repo_id: str, dataset_id=str, base_model=str, train_text_encoder=False, prompt=str, repo_folder=None, vae_path=None, last_checkpoint=str
	):

	yaml = f"""
	---
	base_model: {base_model}
	instance_prompt: {prompt}
	tags:
	- stable-diffusion-xl
	- stable-diffusion-xl-diffusers
	- text-to-image
	- diffusers
	- lora
	inference: false
	datasets:
	- {dataset_id}
	---
	"""
	model_card = f"""
	# LoRA DreamBooth - {repo_id}
	## MODEL IS CURRENTLY TRAINING ...
	Last checkpoint saved: {last_checkpoint}
	These are LoRA adaption weights for {base_model} trained on @fffiloni's SD-XL trainer.
	The weights were trained on the concept prompt:
	```
	{prompt}
	```
	Use this keyword to trigger your custom model in your prompts.
	LoRA for the text encoder was enabled: {train_text_encoder}.
	Special VAE used for training: {vae_path}.
	## Usage
	Make sure to upgrade diffusers to >= 0.19.0:
	```
	pip install diffusers --upgrade
	```
	In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
	```
	pip install invisible_watermark transformers accelerate safetensors
	```
	To just use the base model, you can run:
	```python
	import torch
	from diffusers import DiffusionPipeline, AutoencoderKL
	device = "cuda" if torch.cuda.is_available() else "cpu"
	vae = AutoencoderKL.from_pretrained('{vae_path}', torch_dtype=torch.float16)
	pipe = DiffusionPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0",
	vae=vae, torch_dtype=torch.float16, variant="fp16",
	use_safetensors=True
	)
	pipe.to(device)
	# This is where you load your trained weights
	specific_safetensors = "pytorch_lora_weights.safetensors"
	lora_scale = 0.9
	pipe.load_lora_weights(
	'{repo_id}',
	weight_name = specific_safetensors,
	# use_auth_token = True
	)
	prompt = "A majestic {prompt} jumping from a big stone at night"
	image = pipe(
	prompt=prompt,
	num_inference_steps=50,
	cross_attention_kwargs={{"scale": lora_scale}}
	).images[0]
	```
	"""
	with open(os.path.join(repo_folder, "README.md"), "w") as f:
	f.write(yaml + model_card)

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

	yaml = f"""
	---
	base_model: {base_model}
	instance_prompt: {prompt}
	tags:
	- stable-diffusion-xl
	- stable-diffusion-xl-diffusers
	- text-to-image
	- diffusers
	- lora
	inference: false
	datasets:
	- {dataset_id}
	---
	"""
	model_card = f"""
	# LoRA DreamBooth - {repo_id}
	These are LoRA adaption weights for {base_model} trained on @fffiloni's SD-XL trainer.
	The weights were trained on the concept prompt:
	```
	{prompt}
	```
	Use this keyword to trigger your custom model in your prompts.
	LoRA for the text encoder was enabled: {train_text_encoder}.
	Special VAE used for training: {vae_path}.
	## Usage
	Make sure to upgrade diffusers to >= 0.19.0:
	```
	pip install diffusers --upgrade
	```
	In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
	```
	pip install invisible_watermark transformers accelerate safetensors
	```
	To just use the base model, you can run:
	```python
	import torch
	from diffusers import DiffusionPipeline, AutoencoderKL
	device = "cuda" if torch.cuda.is_available() else "cpu"
	vae = AutoencoderKL.from_pretrained('{vae_path}', torch_dtype=torch.float16)
	pipe = DiffusionPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0",
	vae=vae, torch_dtype=torch.float16, variant="fp16",
	use_safetensors=True
	)
	pipe.to(device)
	# This is where you load your trained weights
	specific_safetensors = "pytorch_lora_weights.safetensors"
	lora_scale = 0.9
	pipe.load_lora_weights(
	'{repo_id}',
	weight_name = specific_safetensors,
	# use_auth_token = True
	)
	prompt = "A majestic {prompt} jumping from a big stone at night"
	image = pipe(
	prompt=prompt,
	num_inference_steps=50,
	cross_attention_kwargs={{"scale": lora_scale}}
	).images[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, subfolder: str = "text_encoder"
	):
	text_encoder_config = PretrainedConfig.from_pretrained(
	pretrained_model_name_or_path, subfolder=subfolder, revision=revision
	)
	model_class = text_encoder_config.architectures[0]

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

	return CLIPTextModel
	elif model_class == "CLIPTextModelWithProjection":
	from transformers import CLIPTextModelWithProjection

	return CLIPTextModelWithProjection
	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=None,
	required=True,
	help="Path to pretrained model or model identifier from huggingface.co/models.",
	)
	parser.add_argument(
	"--pretrained_vae_model_name_or_path",
	type=str,
	default=None,
	help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
	)
	parser.add_argument(
	"--revision",
	type=str,
	default=None,
	required=False,
	help="Revision of pretrained model identifier from huggingface.co/models.",
	)
	parser.add_argument(
	"--dataset_id",
	type=str,
	default=None,
	required=True,
	help="The dataset ID you want to train images from",
	)
	parser.add_argument(
	"--instance_data_dir",
	type=str,
	default=None,
	required=True,
	help="A folder containing the training data of instance images.",
	)
	parser.add_argument(
	"--class_data_dir",
	type=str,
	default=None,
	required=False,
	help="A folder containing the training data of class images.",
	)
	parser.add_argument(
	"--instance_prompt",
	type=str,
	default=None,
	required=True,
	help="The prompt with identifier specifying the instance",
	)
	parser.add_argument(
	"--class_prompt",
	type=str,
	default=None,
	help="The prompt to specify images in the same class as provided instance images.",
	)
	parser.add_argument(
	"--validation_prompt",
	type=str,
	default=None,
	help="A prompt that is used during validation to verify that the model is learning.",
	)
	parser.add_argument(
	"--num_validation_images",
	type=int,
	default=4,
	help="Number of images that should be generated during validation with `validation_prompt`.",
	)
	parser.add_argument(
	"--validation_epochs",
	type=int,
	default=50,
	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(
	"--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="lora-dreambooth-model",
	help="The output directory where the model predictions and checkpoints will be written.",
	)
	parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
	parser.add_argument(
	"--resolution",
	type=int,
	default=1024,
	help=(
	"The resolution for input images, all the images in the train/validation dataset will be resized to this"
	" resolution"
	),
	)
	parser.add_argument(
	"--crops_coords_top_left_h",
	type=int,
	default=0,
	help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
	)
	parser.add_argument(
	"--crops_coords_top_left_w",
	type=int,
	default=0,
	help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
	)
	parser.add_argument(
	"--center_crop",
	default=False,
	action="store_true",
	help=(
	"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
	" cropped. The images will be resized to the resolution first before cropping."
	),
	)
	parser.add_argument(
	"--train_text_encoder",
	action="store_true",
	help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
	)
	parser.add_argument(
	"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
	)
	parser.add_argument(
	"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
	)
	parser.add_argument("--num_train_epochs", type=int, default=1)
	parser.add_argument(
	"--max_train_steps",
	type=int,
	default=None,
	help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
	)
	parser.add_argument(
	"--checkpointing_steps",
	type=int,
	default=500,
	help=(
	"Save a checkpoint of the training state every X updates. 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."),
	)
	parser.add_argument(
	"--resume_from_checkpoint",
	type=str,
	default=None,
	help=(
	"Whether training should be resumed from a previous checkpoint. Use a path saved by"
	' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
	),
	)
	parser.add_argument(
	"--gradient_accumulation_steps",
	type=int,
	default=1,
	help="Number of updates steps to accumulate before performing a backward/update pass.",
	)
	parser.add_argument(
	"--gradient_checkpointing",
	action="store_true",
	help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
	)
	parser.add_argument(
	"--learning_rate",
	type=float,
	default=5e-4,
	help="Initial learning rate (after the potential warmup period) to use.",
	)
	parser.add_argument(
	"--scale_lr",
	action="store_true",
	default=False,
	help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
	)
	parser.add_argument(
	"--lr_scheduler",
	type=str,
	default="constant",
	help=(
	'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
	' "constant", "constant_with_warmup"]'
	),
	)
	parser.add_argument(
	"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
	)
	parser.add_argument(
	"--lr_num_cycles",
	type=int,
	default=1,
	help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
	)
	parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
	parser.add_argument(
	"--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(
	"--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("--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(
	"--rank",
	type=int,
	default=4,
	help=("The dimension of the LoRA update matrices."),
	)

	if input_args is not None:
	args = parser.parse_args(input_args)
	else:
	args = parser.parse_args()

	env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
	if env_local_rank != -1 and env_local_rank != args.local_rank:
	args.local_rank = env_local_rank

	if args.with_prior_preservation:
	if args.class_data_dir is None:
	raise ValueError("You must specify a data directory for class images.")
	if args.class_prompt is None:
	raise ValueError("You must specify prompt for class images.")
	else:
	# logger is not available yet
	if args.class_data_dir is not None:
	warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
	if args.class_prompt is not None:
	warnings.warn("You need not use --class_prompt without --with_prior_preservation.")

	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.
	"""

	def __init__(
	self,
	instance_data_root,
	class_data_root=None,
	class_num=None,
	size=1024,
	center_crop=False,
	):
	self.size = size
	self.center_crop = center_crop

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

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

	if class_data_root is not None:
	self.class_data_root = Path(class_data_root)
	self.class_data_root.mkdir(parents=True, exist_ok=True)
	self.class_images_path = list(self.class_data_root.iterdir())
	if class_num is not None:
	self.num_class_images = min(len(self.class_images_path), class_num)
	else:
	self.num_class_images = len(self.class_images_path)
	self._length = max(self.num_class_images, self.num_instance_images)
	else:
	self.class_data_root = None

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

	def __len__(self):
	return self._length

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

	if not instance_image.mode == "RGB":
	instance_image = instance_image.convert("RGB")
	example["instance_images"] = self.image_transforms(instance_image)

	if self.class_data_root:
	class_image = Image.open(self.class_images_path[index % self.num_class_images])
	class_image = exif_transpose(class_image)

	if not class_image.mode == "RGB":
	class_image = class_image.convert("RGB")
	example["class_images"] = self.image_transforms(class_image)

	return example


	def collate_fn(examples, with_prior_preservation=False):
	pixel_values = [example["instance_images"] for example in examples]

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

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

	batch = {"pixel_values": pixel_values}
	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 tokenize_prompt(tokenizer, prompt):
	text_inputs = tokenizer(
	prompt,
	padding="max_length",
	max_length=tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)
	text_input_ids = text_inputs.input_ids
	return text_input_ids


	# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
	def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
	prompt_embeds_list = []

	for i, text_encoder in enumerate(text_encoders):
	if tokenizers is not None:
	tokenizer = tokenizers[i]
	text_input_ids = tokenize_prompt(tokenizer, prompt)
	else:
	assert text_input_ids_list is not None
	text_input_ids = text_input_ids_list[i]

	prompt_embeds = text_encoder(
	text_input_ids.to(text_encoder.device),
	output_hidden_states=True,
	)

	# We are only ALWAYS interested in the pooled output of the final text encoder
	pooled_prompt_embeds = prompt_embeds[0]
	prompt_embeds = prompt_embeds.hidden_states[-2]
	bs_embed, seq_len, _ = prompt_embeds.shape
	prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
	prompt_embeds_list.append(prompt_embeds)

	prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
	pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
	return prompt_embeds, pooled_prompt_embeds


	def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
	"""
	Returns:
	a state dict containing just the attention processor parameters.
	"""
	attn_processors = unet.attn_processors

	attn_processors_state_dict = {}

	for attn_processor_key, attn_processor in attn_processors.items():
	for parameter_key, parameter in attn_processor.state_dict().items():
	attn_processors_state_dict[f"{attn_processor_key}.{parameter_key}"] = parameter

	return attn_processors_state_dict


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

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

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

	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

	# Make one log on every process with the configuration for debugging.
	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	datefmt="%m/%d/%Y %H:%M:%S",
	level=logging.INFO,
	)
	logger.info(accelerator.state, main_process_only=False)
	if accelerator.is_local_main_process:
	transformers.utils.logging.set_verbosity_warning()
	diffusers.utils.logging.set_verbosity_info()
	else:
	transformers.utils.logging.set_verbosity_error()
	diffusers.utils.logging.set_verbosity_error()

	# If passed along, set the training seed now.
	if args.seed is not None:
	set_seed(args.seed)

	# Generate class images if prior preservation is enabled.
	if args.with_prior_preservation:
	class_images_dir = Path(args.class_data_dir)
	if not class_images_dir.exists():
	class_images_dir.mkdir(parents=True)
	cur_class_images = len(list(class_images_dir.iterdir()))

	if cur_class_images < args.num_class_images:
	torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
	if args.prior_generation_precision == "fp32":
	torch_dtype = torch.float32
	elif args.prior_generation_precision == "fp16":
	torch_dtype = torch.float16
	elif args.prior_generation_precision == "bf16":
	torch_dtype = torch.bfloat16
	pipeline = StableDiffusionXLPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	torch_dtype=torch_dtype,
	revision=args.revision,
	)
	pipeline.set_progress_bar_config(disable=True)

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

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

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

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

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

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

	# Handle the repository creation
	if accelerator.is_main_process:
	if args.output_dir is not None:
	os.makedirs(args.output_dir, exist_ok=True)

	if args.push_to_hub:
	repo_id = create_repo(
	repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, private=True, token=args.hub_token
	).repo_id

	# Load the tokenizers
	tokenizer_one = AutoTokenizer.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
	)
	tokenizer_two = AutoTokenizer.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
	)

	# import correct text encoder classes
	text_encoder_cls_one = import_model_class_from_model_name_or_path(
	args.pretrained_model_name_or_path, args.revision
	)
	text_encoder_cls_two = import_model_class_from_model_name_or_path(
	args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
	)

	# Load scheduler and models
	noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
	text_encoder_one = text_encoder_cls_one.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
	)
	text_encoder_two = text_encoder_cls_two.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
	)
	vae_path = (
	args.pretrained_model_name_or_path
	if args.pretrained_vae_model_name_or_path is None
	else args.pretrained_vae_model_name_or_path
	)
	vae = AutoencoderKL.from_pretrained(
	vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
	)
	unet = UNet2DConditionModel.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
	)

	# We only train the additional adapter LoRA layers
	vae.requires_grad_(False)
	text_encoder_one.requires_grad_(False)
	text_encoder_two.requires_grad_(False)
	unet.requires_grad_(False)

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

	# Move unet, vae and text_encoder to device and cast to weight_dtype
	unet.to(accelerator.device, dtype=weight_dtype)

	# The VAE is always in float32 to avoid NaN losses.
	vae.to(accelerator.device, dtype=torch.float32)

	text_encoder_one.to(accelerator.device, dtype=weight_dtype)
	text_encoder_two.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.train_text_encoder:
	text_encoder_one.gradient_checkpointing_enable()
	text_encoder_two.gradient_checkpointing_enable()

	# now we will add new LoRA weights to the attention layers
	# Set correct lora layers
	unet_lora_attn_procs = {}
	unet_lora_parameters = []
	for name, attn_processor in unet.attn_processors.items():
	cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
	if name.startswith("mid_block"):
	hidden_size = unet.config.block_out_channels[-1]
	elif name.startswith("up_blocks"):
	block_id = int(name[len("up_blocks.")])
	hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
	elif name.startswith("down_blocks"):
	block_id = int(name[len("down_blocks.")])
	hidden_size = unet.config.block_out_channels[block_id]

	lora_attn_processor_class = (
	LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
	)
	module = lora_attn_processor_class(
	hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.rank
	)
	unet_lora_attn_procs[name] = module
	unet_lora_parameters.extend(module.parameters())

	unet.set_attn_processor(unet_lora_attn_procs)

	# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
	# So, instead, we monkey-patch the forward calls of its attention-blocks.
	if args.train_text_encoder:
	# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
	text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder(
	text_encoder_one, dtype=torch.float32, rank=args.rank
	)
	text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder(
	text_encoder_two, dtype=torch.float32, rank=args.rank
	)

	# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
	def save_model_hook(models, weights, output_dir):
	if accelerator.is_main_process:
	# there are only two options here. Either are just the unet attn processor layers
	# or there are the unet and text encoder atten layers
	unet_lora_layers_to_save = None
	text_encoder_one_lora_layers_to_save = None
	text_encoder_two_lora_layers_to_save = None

	for model in models:
	if isinstance(model, type(accelerator.unwrap_model(unet))):
	unet_lora_layers_to_save = unet_attn_processors_state_dict(model)
	elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
	text_encoder_one_lora_layers_to_save = text_encoder_lora_state_dict(model)
	elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
	text_encoder_two_lora_layers_to_save = text_encoder_lora_state_dict(model)
	else:
	raise ValueError(f"unexpected save model: {model.__class__}")

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

	StableDiffusionXLPipeline.save_lora_weights(
	output_dir,
	unet_lora_layers=unet_lora_layers_to_save,
	text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
	text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
	)

	def load_model_hook(models, input_dir):
	unet_ = None
	text_encoder_one_ = None
	text_encoder_two_ = None

	while len(models) > 0:
	model = models.pop()

	if isinstance(model, type(accelerator.unwrap_model(unet))):
	unet_ = model
	elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
	text_encoder_one_ = model
	elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
	text_encoder_two_ = model
	else:
	raise ValueError(f"unexpected save model: {model.__class__}")

	lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
	LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)

	text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k}
	LoraLoaderMixin.load_lora_into_text_encoder(
	text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
	)

	text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k}
	LoraLoaderMixin.load_lora_into_text_encoder(
	text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
	)

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

	# Enable TF32 for faster training on Ampere GPUs,
	# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
	if args.allow_tf32:
	torch.backends.cuda.matmul.allow_tf32 = True

	if args.scale_lr:
	args.learning_rate = (
	args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
	)

	# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
	if args.use_8bit_adam:
	try:
	import bitsandbytes as bnb
	except ImportError:
	raise ImportError(
	"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
	)

	optimizer_class = bnb.optim.AdamW8bit
	else:
	optimizer_class = torch.optim.AdamW

	# Optimizer creation
	params_to_optimize = (
	itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
	if args.train_text_encoder
	else unet_lora_parameters
	)
	optimizer = optimizer_class(
	params_to_optimize,
	lr=args.learning_rate,
	betas=(args.adam_beta1, args.adam_beta2),
	weight_decay=args.adam_weight_decay,
	eps=args.adam_epsilon,
	)

	# Computes additional embeddings/ids required by the SDXL UNet.
	# regular text emebddings (when `train_text_encoder` is not True)
	# pooled text embeddings
	# time ids

	def compute_time_ids():
	# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
	original_size = (args.resolution, args.resolution)
	target_size = (args.resolution, args.resolution)
	crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
	add_time_ids = list(original_size + crops_coords_top_left + target_size)
	add_time_ids = torch.tensor([add_time_ids])
	add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
	return add_time_ids

	if not args.train_text_encoder:
	tokenizers = [tokenizer_one, tokenizer_two]
	text_encoders = [text_encoder_one, text_encoder_two]

	def compute_text_embeddings(prompt, text_encoders, tokenizers):
	with torch.no_grad():
	prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)
	prompt_embeds = prompt_embeds.to(accelerator.device)
	pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
	return prompt_embeds, pooled_prompt_embeds

	# Handle instance prompt.
	instance_time_ids = compute_time_ids()
	if not args.train_text_encoder:
	instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(
	args.instance_prompt, text_encoders, tokenizers
	)

	# Handle class prompt for prior-preservation.
	if args.with_prior_preservation:
	class_time_ids = compute_time_ids()
	if not args.train_text_encoder:
	class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(
	args.class_prompt, text_encoders, tokenizers
	)

	# Clear the memory here.
	if not args.train_text_encoder:
	del tokenizers, text_encoders
	gc.collect()
	torch.cuda.empty_cache()

	# Pack the statically computed variables appropriately. This is so that we don't
	# have to pass them to the dataloader.
	add_time_ids = instance_time_ids
	if args.with_prior_preservation:
	add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0)

	if not args.train_text_encoder:
	prompt_embeds = instance_prompt_hidden_states
	unet_add_text_embeds = instance_pooled_prompt_embeds
	if args.with_prior_preservation:
	prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
	unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
	else:
	tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)
	tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)
	if args.with_prior_preservation:
	class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)
	class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)
	tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
	tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)

	# Dataset and DataLoaders creation:
	train_dataset = DreamBoothDataset(
	instance_data_root=args.instance_data_dir,
	class_data_root=args.class_data_dir if args.with_prior_preservation else None,
	class_num=args.num_class_images,
	size=args.resolution,
	center_crop=args.center_crop,
	)

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

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

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

	# Prepare everything with our `accelerator`.
	if args.train_text_encoder:
	unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet, text_encoder_one, text_encoder_two, 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)

	# We need to initialize the trackers we use, and also store our configuration.
	# The trackers initializes automatically on the main process.
	if accelerator.is_main_process:
	accelerator.init_trackers("dreambooth-lora-sd-xl", config=vars(args))

	# 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
	initial_global_step = 0
	else:
	accelerator.print(f"Resuming from checkpoint {path}")
	accelerator.load_state(os.path.join(args.output_dir, path))
	global_step = int(path.split("-")[1])

	initial_global_step = global_step
	first_epoch = global_step // num_update_steps_per_epoch

	else:
	initial_global_step = 0

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

	for epoch in range(first_epoch, args.num_train_epochs):
	# Print a message for each epoch
	print(f"Epoch {epoch}: Training in progress...")
	unet.train()
	if args.train_text_encoder:
	text_encoder_one.train()
	text_encoder_two.train()
	for step, batch in enumerate(train_dataloader):
	with accelerator.accumulate(unet):
	pixel_values = batch["pixel_values"].to(dtype=vae.dtype)

	# Convert images to latent space
	model_input = vae.encode(pixel_values).latent_dist.sample()
	model_input = model_input * vae.config.scaling_factor
	if args.pretrained_vae_model_name_or_path is None:
	model_input = model_input.to(weight_dtype)

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

	# Add noise to the model input according to the noise magnitude at each timestep
	# (this is the forward diffusion process)
	noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)

	# Calculate the elements to repeat depending on the use of prior-preservation.
	elems_to_repeat = bsz // 2 if args.with_prior_preservation else bsz

	# Predict the noise residual
	if not args.train_text_encoder:
	unet_added_conditions = {
	"time_ids": add_time_ids.repeat(elems_to_repeat, 1),
	"text_embeds": unet_add_text_embeds.repeat(elems_to_repeat, 1),
	}
	prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1)
	model_pred = unet(
	noisy_model_input,
	timesteps,
	prompt_embeds_input,
	added_cond_kwargs=unet_added_conditions,
	).sample
	else:
	unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat, 1)}
	prompt_embeds, pooled_prompt_embeds = encode_prompt(
	text_encoders=[text_encoder_one, text_encoder_two],
	tokenizers=None,
	prompt=None,
	text_input_ids_list=[tokens_one, tokens_two],
	)
	unet_added_conditions.update({"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat, 1)})
	prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat, 1, 1)
	model_pred = unet(
	noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
	).sample

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

	if args.with_prior_preservation:
	# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
	model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
	target, target_prior = torch.chunk(target, 2, dim=0)

	# Compute 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 + args.prior_loss_weight * prior_loss
	else:
	loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

	accelerator.backward(loss)
	if accelerator.sync_gradients:
	params_to_clip = (
	itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
	if args.train_text_encoder
	else unet_lora_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:
	# Print a message for each step
	print(f"Step {global_step}/{args.max_train_steps}: Done")
	progress_bar.update(1)
	global_step += 1

	if accelerator.is_main_process:
	if global_step % args.checkpointing_steps == 0:
	# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
	if args.checkpoints_total_limit is not None:
	checkpoints = os.listdir(args.output_dir)
	checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
	checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

	# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
	if len(checkpoints) >= args.checkpoints_total_limit:
	num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
	removing_checkpoints = checkpoints[0:num_to_remove]

	logger.info(
	f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
	)
	logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

	for removing_checkpoint in removing_checkpoints:
	removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
	shutil.rmtree(removing_checkpoint)

	save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
	accelerator.save_state(save_path)
	logger.info(f"Saved state to {save_path}")

	save_tempo_model_card(
	repo_id,
	dataset_id=args.dataset_id,
	base_model=args.pretrained_model_name_or_path,
	train_text_encoder=args.train_text_encoder,
	prompt=args.instance_prompt,
	repo_folder=args.output_dir,
	vae_path=args.pretrained_vae_model_name_or_path,
	last_checkpoint = f"checkpoint-{global_step}"
	)

	upload_folder(
	repo_id=repo_id,
	folder_path=args.output_dir,
	commit_message=f"saving checkpoint-{global_step}",
	ignore_patterns=["step_", "epoch_"],
	token=args.hub_token
	)

	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 epoch % args.validation_epochs == 0:
	logger.info(
	f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
	f" {args.validation_prompt}."
	)
	# create pipeline
	if not args.train_text_encoder:
	text_encoder_one = text_encoder_cls_one.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
	)
	text_encoder_two = text_encoder_cls_two.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
	)
	pipeline = StableDiffusionXLPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	vae=vae,
	text_encoder=accelerator.unwrap_model(text_encoder_one),
	text_encoder_2=accelerator.unwrap_model(text_encoder_two),
	unet=accelerator.unwrap_model(unet),
	revision=args.revision,
	torch_dtype=weight_dtype,
	)

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

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

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

	scheduler_args["variance_type"] = variance_type

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

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

	# run inference
	generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
	pipeline_args = {"prompt": args.validation_prompt}

	with torch.cuda.amp.autocast():
	images = [
	pipeline(**pipeline_args, generator=generator).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()

	# Save the lora layers
	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
	unet = accelerator.unwrap_model(unet)
	unet = unet.to(torch.float32)
	unet_lora_layers = unet_attn_processors_state_dict(unet)

	if args.train_text_encoder:
	text_encoder_one = accelerator.unwrap_model(text_encoder_one)
	text_encoder_lora_layers = text_encoder_lora_state_dict(text_encoder_one.to(torch.float32))
	text_encoder_two = accelerator.unwrap_model(text_encoder_two)
	text_encoder_2_lora_layers = text_encoder_lora_state_dict(text_encoder_two.to(torch.float32))
	else:
	text_encoder_lora_layers = None
	text_encoder_2_lora_layers = None

	StableDiffusionXLPipeline.save_lora_weights(
	save_directory=args.output_dir,
	unet_lora_layers=unet_lora_layers,
	text_encoder_lora_layers=text_encoder_lora_layers,
	text_encoder_2_lora_layers=text_encoder_2_lora_layers,
	)

	# Final inference
	# Load previous pipeline
	vae = AutoencoderKL.from_pretrained(
	vae_path,
	subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
	revision=args.revision,
	torch_dtype=weight_dtype,
	)
	pipeline = StableDiffusionXLPipeline.from_pretrained(
	args.pretrained_model_name_or_path, vae=vae, revision=args.revision, torch_dtype=weight_dtype
	)

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

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

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

	scheduler_args["variance_type"] = variance_type

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

	# load attention processors
	pipeline.load_lora_weights(args.output_dir)

	# run inference
	images = []
	if args.validation_prompt and args.num_validation_images > 0:
	pipeline = pipeline.to(accelerator.device)
	generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
	images = [
	pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).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,
	dataset_id=args.dataset_id,
	base_model=args.pretrained_model_name_or_path,
	train_text_encoder=args.train_text_encoder,
	prompt=args.instance_prompt,
	repo_folder=args.output_dir,
	vae_path=args.pretrained_vae_model_name_or_path,
	)
	upload_folder(
	repo_id=repo_id,
	folder_path=args.output_dir,
	commit_message="End of training",
	ignore_patterns=["step_", "epoch_"],
	token=args.hub_token
	)

	accelerator.end_training()

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