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sdpipe_webui / utils /textual_inversion.py

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refactor code and fix cpu support

919fef8 over 1 year ago

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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2022 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 logging
	import math
	import os
	import random
	from pathlib import Path
	from typing import Optional

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

	import datasets
	import diffusers
	import PIL
	import transformers
	from accelerate import Accelerator
	from accelerate.logging import get_logger
	from accelerate.utils import set_seed
	from diffusers import (
	AutoencoderKL,
	DDPMScheduler,
	StableDiffusionPipeline,
	UNet2DConditionModel,
	)
	from diffusers.optimization import get_scheduler
	from diffusers.utils import check_min_version
	from diffusers.utils.import_utils import is_xformers_available
	from huggingface_hub import HfFolder, Repository, create_repo, whoami

	# TODO: remove and import from diffusers.utils when the new version of diffusers is released
	from packaging import version
	from PIL import Image
	from torchvision import transforms
	from tqdm.auto import tqdm
	from transformers import CLIPTextModel, CLIPTokenizer


	if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
	PIL_INTERPOLATION = {
	"linear": PIL.Image.Resampling.BILINEAR,
	"bilinear": PIL.Image.Resampling.BILINEAR,
	"bicubic": PIL.Image.Resampling.BICUBIC,
	"lanczos": PIL.Image.Resampling.LANCZOS,
	"nearest": PIL.Image.Resampling.NEAREST,
	}
	else:
	PIL_INTERPOLATION = {
	"linear": PIL.Image.LINEAR,
	"bilinear": PIL.Image.BILINEAR,
	"bicubic": PIL.Image.BICUBIC,
	"lanczos": PIL.Image.LANCZOS,
	"nearest": PIL.Image.NEAREST,
	}
	# ------------------------------------------------------------------------------


	# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
	check_min_version("0.10.0.dev0")


	logger = get_logger(__name__)


	def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
	logger.info("Saving embeddings")
	learned_embeds = (
	accelerator.unwrap_model(text_encoder)
	.get_input_embeddings()
	.weight[placeholder_token_id]
	)
	learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
	torch.save(learned_embeds_dict, save_path)


	def parse_args():
	parser = argparse.ArgumentParser(description="Simple example of a training script.")
	parser.add_argument(
	"--save_steps",
	type=int,
	default=500,
	help="Save learned_embeds.bin every X updates steps.",
	)
	parser.add_argument(
	"--only_save_embeds",
	action="store_true",
	default=False,
	help="Save only the embeddings for the new concept.",
	)
	parser.add_argument(
	"--pretrained_model_name_or_path",
	type=str,
	default=None,
	help="Path to pretrained model or model identifier from huggingface.co/models.",
	)
	parser.add_argument(
	"--revision",
	type=str,
	default=None,
	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(
	"--train_data_dir",
	type=str,
	default=None,
	help="A folder containing the training data.",
	)
	parser.add_argument(
	"--placeholder_token",
	type=str,
	default=None,
	help="A token to use as a placeholder for the concept.",
	)
	parser.add_argument(
	"--initializer_token",
	type=str,
	default=None,
	help="A token to use as initializer word.",
	)

	parser.add_argument(
	"--learnable_property",
	type=str,
	default="object",
	help="Choose between 'object' and 'style'",
	)
	parser.add_argument(
	"--repeats",
	type=int,
	default=100,
	help="How many times to repeat the training data.",
	)
	parser.add_argument(
	"--output_dir",
	type=str,
	default="text-inversion-model",
	help="The output directory where the model predictions and checkpoints will be written.",
	)
	parser.add_argument(
	"--seed", type=int, default=None, help="A seed for reproducible training."
	)
	parser.add_argument(
	"--resolution",
	type=int,
	default=512,
	help=(
	"The resolution for input images, all the images in the train/validation dataset will be resized to this"
	" resolution"
	),
	)
	parser.add_argument(
	"--center_crop",
	action="store_true",
	help="Whether to center crop images before resizing to resolution",
	)
	parser.add_argument(
	"--train_batch_size",
	type=int,
	default=16,
	help="Batch size (per device) for the training dataloader.",
	)
	parser.add_argument("--num_train_epochs", type=int, default=100)
	parser.add_argument(
	"--max_train_steps",
	type=int,
	default=5000,
	help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
	)
	parser.add_argument(
	"--gradient_accumulation_steps",
	type=int,
	default=1,
	help="Number of updates steps to accumulate before performing a backward/update pass.",
	)
	parser.add_argument(
	"--gradient_checkpointing",
	action="store_true",
	help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
	)
	parser.add_argument(
	"--learning_rate",
	type=float,
	default=1e-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(
	"--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(
	"--push_to_hub",
	action="store_true",
	help="Whether or not to push the model to the Hub.",
	)
	parser.add_argument(
	"--hub_token",
	type=str,
	default=None,
	help="The token to use to push to the Model Hub.",
	)
	parser.add_argument(
	"--hub_model_id",
	type=str,
	default=None,
	help="The name of the repository to keep in sync with the local `output_dir`.",
	)
	parser.add_argument(
	"--logging_dir",
	type=str,
	default="logs",
	help=(
	"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
	" output_dir/runs/CURRENT_DATETIME_HOSTNAME**."
	),
	)
	parser.add_argument(
	"--mixed_precision",
	type=str,
	default="no",
	choices=["no", "fp16", "bf16"],
	help=(
	"Whether to use mixed precision. Choose"
	"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	"and an Nvidia Ampere GPU."
	),
	)
	parser.add_argument(
	"--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(
	"--local_rank",
	type=int,
	default=-1,
	help="For distributed training: local_rank",
	)
	parser.add_argument(
	"--checkpointing_steps",
	type=int,
	default=500,
	help=(
	"Save a checkpoint of the training state every X updates. These checkpoints are only 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(
	"--enable_xformers_memory_efficient_attention",
	action="store_true",
	help="Whether or not to use xformers.",
	)

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

	return args


	imagenet_templates_small = [
	"a photo of a {}",
	"a rendering of a {}",
	"a cropped photo of the {}",
	"the photo of a {}",
	"a photo of a clean {}",
	"a photo of a dirty {}",
	"a dark photo of the {}",
	"a photo of my {}",
	"a photo of the cool {}",
	"a close-up photo of a {}",
	"a bright photo of the {}",
	"a cropped photo of a {}",
	"a photo of the {}",
	"a good photo of the {}",
	"a photo of one {}",
	"a close-up photo of the {}",
	"a rendition of the {}",
	"a photo of the clean {}",
	"a rendition of a {}",
	"a photo of a nice {}",
	"a good photo of a {}",
	"a photo of the nice {}",
	"a photo of the small {}",
	"a photo of the weird {}",
	"a photo of the large {}",
	"a photo of a cool {}",
	"a photo of a small {}",
	]

	imagenet_style_templates_small = [
	"a painting in the style of {}",
	"a rendering in the style of {}",
	"a cropped painting in the style of {}",
	"the painting in the style of {}",
	"a clean painting in the style of {}",
	"a dirty painting in the style of {}",
	"a dark painting in the style of {}",
	"a picture in the style of {}",
	"a cool painting in the style of {}",
	"a close-up painting in the style of {}",
	"a bright painting in the style of {}",
	"a cropped painting in the style of {}",
	"a good painting in the style of {}",
	"a close-up painting in the style of {}",
	"a rendition in the style of {}",
	"a nice painting in the style of {}",
	"a small painting in the style of {}",
	"a weird painting in the style of {}",
	"a large painting in the style of {}",
	]


	class TextualInversionDataset(Dataset):
	def __init__(
	self,
	data_root,
	tokenizer,
	learnable_property="object", # [object, style]
	size=512,
	repeats=100,
	interpolation="bicubic",
	flip_p=0.5,
	set="train",
	placeholder_token="*",
	center_crop=False,
	):
	self.data_root = data_root
	self.tokenizer = tokenizer
	self.learnable_property = learnable_property
	self.size = size
	self.placeholder_token = placeholder_token
	self.center_crop = center_crop
	self.flip_p = flip_p

	self.image_paths = [
	os.path.join(self.data_root, file_path)
	for file_path in os.listdir(self.data_root)
	]

	self.num_images = len(self.image_paths)
	self._length = self.num_images

	if set == "train":
	self._length = self.num_images * repeats

	self.interpolation = {
	"linear": PIL_INTERPOLATION["linear"],
	"bilinear": PIL_INTERPOLATION["bilinear"],
	"bicubic": PIL_INTERPOLATION["bicubic"],
	"lanczos": PIL_INTERPOLATION["lanczos"],
	}[interpolation]

	self.templates = (
	imagenet_style_templates_small
	if learnable_property == "style"
	else imagenet_templates_small
	)
	self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)

	def __len__(self):
	return self._length

	def __getitem__(self, i):
	example = {}
	image = Image.open(self.image_paths[i % self.num_images])

	if not image.mode == "RGB":
	image = image.convert("RGB")

	placeholder_string = self.placeholder_token
	text = random.choice(self.templates).format(placeholder_string)

	example["input_ids"] = self.tokenizer(
	text,
	padding="max_length",
	truncation=True,
	max_length=self.tokenizer.model_max_length,
	return_tensors="pt",
	).input_ids[0]

	# default to score-sde preprocessing
	img = np.array(image).astype(np.uint8)

	if self.center_crop:
	crop = min(img.shape[0], img.shape[1])
	(h, w,) = (
	img.shape[0],
	img.shape[1],
	)
	img = img[
	(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
	]

	image = Image.fromarray(img)
	image = image.resize((self.size, self.size), resample=self.interpolation)

	image = self.flip_transform(image)
	image = np.array(image).astype(np.uint8)
	image = (image / 127.5 - 1.0).astype(np.float32)

	example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
	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}"


	def main(pipe, args_imported):

	args = parse_args()
	vars(args).update(vars(args_imported))

	print(args)
	logging_dir = os.path.join(args.output_dir, args.logging_dir)

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

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

	# Handle the repository creation
	if accelerator.is_main_process:
	if args.push_to_hub:
	if args.hub_model_id is None:
	repo_name = get_full_repo_name(
	Path(args.output_dir).name, token=args.hub_token
	)
	else:
	repo_name = args.hub_model_id
	create_repo(repo_name, exist_ok=True, token=args.hub_token)
	repo = Repository(
	args.output_dir, clone_from=repo_name, token=args.hub_token
	)

	with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
	if "step_*" not in gitignore:
	gitignore.write("step_*\n")
	if "epoch_*" not in gitignore:
	gitignore.write("epoch_*\n")
	elif args.output_dir is not None:
	os.makedirs(args.output_dir, exist_ok=True)

	# Load tokenizer
	tokenizer = pipe.tokenizer

	# Load scheduler and models
	noise_scheduler = pipe.scheduler
	text_encoder = pipe.text_encoder
	vae = pipe.vae
	unet = pipe.unet

	# Add the placeholder token in tokenizer
	num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
	if num_added_tokens == 0:
	raise ValueError(
	f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
	" `placeholder_token` that is not already in the tokenizer."
	)

	# Convert the initializer_token, placeholder_token to ids
	token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
	# Check if initializer_token is a single token or a sequence of tokens
	if len(token_ids) > 1:
	raise ValueError("The initializer token must be a single token.")

	initializer_token_id = token_ids[0]
	placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)

	# Resize the token embeddings as we are adding new special tokens to the tokenizer
	text_encoder.resize_token_embeddings(len(tokenizer))

	# Initialise the newly added placeholder token with the embeddings of the initializer token
	token_embeds = text_encoder.get_input_embeddings().weight.data
	token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]

	# Freeze vae and unet
	vae.requires_grad_(False)
	unet.requires_grad_(False)
	# Freeze all parameters except for the token embeddings in text encoder
	text_encoder.text_model.encoder.requires_grad_(False)
	text_encoder.text_model.final_layer_norm.requires_grad_(False)
	text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)

	if args.gradient_checkpointing:
	# Keep unet in train mode if we are using gradient checkpointing to save memory.
	# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
	unet.train()
	text_encoder.gradient_checkpointing_enable()
	unet.enable_gradient_checkpointing()

	if args.enable_xformers_memory_efficient_attention:
	if is_xformers_available():
	unet.enable_xformers_memory_efficient_attention()
	else:
	raise ValueError(
	"xformers is not available. Make sure it is installed correctly"
	)

	# 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
	)

	# Initialize the optimizer
	optimizer = torch.optim.AdamW(
	text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
	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 = TextualInversionDataset(
	data_root=args.train_data_dir,
	tokenizer=tokenizer,
	size=args.resolution,
	placeholder_token=args.placeholder_token,
	repeats=args.repeats,
	learnable_property=args.learnable_property,
	center_crop=args.center_crop,
	set="train",
	)
	train_dataloader = torch.utils.data.DataLoader(
	train_dataset, batch_size=args.train_batch_size, shuffle=True
	)

	# 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`.
	text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	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.
	weight_dtype = torch.float32
	if accelerator.mixed_precision == "fp16":
	weight_dtype = torch.float16
	elif accelerator.mixed_precision == "bf16":
	weight_dtype = torch.bfloat16

	# Move vae and unet to device and cast to weight_dtype
	unet.to(accelerator.device, dtype=weight_dtype)
	vae.to(accelerator.device, dtype=weight_dtype)
	text_encoder.to(accelerator.device, dtype=torch.float32)

	# 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("textual_inversion", 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 Epochs = {args.num_train_epochs}")
	logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
	logger.info(
	f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
	)
	logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
	logger.info(f" Total optimization steps = {args.max_train_steps}")
	global_step = 0
	first_epoch = 0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
	if args.resume_from_checkpoint != "latest":
	path = os.path.basename(args.resume_from_checkpoint)
	else:
	# Get the most recent checkpoint
	dirs = os.listdir(args.output_dir)
	dirs = [d for d in dirs if d.startswith("checkpoint")]
	dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
	path = dirs[-1]
	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 = resume_global_step // num_update_steps_per_epoch
	resume_step = resume_global_step % num_update_steps_per_epoch

	# 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")

	# keep original embeddings as reference
	orig_embeds_params = (
	accelerator.unwrap_model(text_encoder)
	.get_input_embeddings()
	.weight.data.clone()
	)

	for epoch in range(first_epoch, args.num_train_epochs):
	text_encoder.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(text_encoder):
	# Convert images to latent space
	latents = (
	vae.encode(batch["pixel_values"].to(dtype=weight_dtype))
	.latent_dist.sample()
	.detach()
	)
	latents = latents * 0.18215

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

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

	# Get the text embedding for conditioning
	encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(
	dtype=weight_dtype
	)

	# Predict the noise residual
	model_pred = unet(
	noisy_latents, timesteps, encoder_hidden_states
	).sample

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

	loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

	accelerator.backward(loss)

	if accelerator.num_processes > 1:
	grads = text_encoder.module.get_input_embeddings().weight.grad
	else:
	grads = 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)) != placeholder_token_id
	)
	grads.data[index_grads_to_zero, :] = grads.data[
	index_grads_to_zero, :
	].fill_(0)

	optimizer.step()
	lr_scheduler.step()
	optimizer.zero_grad()

	# Let's make sure we don't update any embedding weights besides the newly added token
	index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
	with torch.no_grad():
	accelerator.unwrap_model(
	text_encoder
	).get_input_embeddings().weight[
	index_no_updates
	] = orig_embeds_params[
	index_no_updates
	]

	# 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.save_steps == 0:
	save_path = os.path.join(
	args.output_dir, f"{args.placeholder_token}-{global_step}.bin"
	)
	save_progress(
	text_encoder, placeholder_token_id, accelerator, args, save_path
	)

	if global_step % args.checkpointing_steps == 0:
	if accelerator.is_main_process:
	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}")

	logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
	progress_bar.set_postfix(**logs)
	accelerator.log(logs, step=global_step)

	if global_step >= args.max_train_steps:
	break

	# Create the pipeline using using the trained modules and save it.
	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
	if args.push_to_hub and args.only_save_embeds:
	logger.warn(
	"Enabling full model saving because --push_to_hub=True was specified."
	)
	save_full_model = True
	else:
	save_full_model = not args.only_save_embeds
	if save_full_model:
	pipe.save_pretrained(args.output_dir)
	# Save the newly trained embeddings
	save_path = os.path.join(args.output_dir, "learned_embeds.bin")
	save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)

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

	accelerator.end_training()


	if __name__ == "__main__":
	pipeline = StableDiffusionPipeline.from_pretrained(
	"andite/anything-v4.0", torch_dtype=torch.float16
	)

	imported_args = argparse.Namespace(
	train_data_dir="concept_images",
	learnable_property="object",
	placeholder_token="redeyegirl",
	initializer_token="girl",
	resolution=512,
	train_batch_size=1,
	gradient_accumulation_steps=1,
	gradient_checkpointing=True,
	mixed_precision="fp16",
	use_bf16=False,
	max_train_steps=1000,
	learning_rate=5.0e-4,
	scale_lr=False,
	lr_scheduler="constant",
	lr_warmup_steps=0,
	output_dir="output_model",
	)

	main(pipeline, imported_args)