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tango2
/
diffusers
/examples
/research_projects
/multi_subject_dreambooth
/train_multi_subject_dreambooth.py
import argparse | |
import hashlib | |
import itertools | |
import logging | |
import math | |
import os | |
import warnings | |
from pathlib import Path | |
import datasets | |
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 PIL import Image | |
from torch.utils.data import Dataset | |
from torchvision import transforms | |
from tqdm.auto import tqdm | |
from transformers import AutoTokenizer, PretrainedConfig | |
import diffusers | |
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel | |
from diffusers.optimization import get_scheduler | |
from diffusers.utils import check_min_version | |
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("0.13.0.dev0") | |
logger = get_logger(__name__) | |
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): | |
text_encoder_config = PretrainedConfig.from_pretrained( | |
pretrained_model_name_or_path, | |
subfolder="text_encoder", | |
revision=revision, | |
) | |
model_class = text_encoder_config.architectures[0] | |
if model_class == "CLIPTextModel": | |
from transformers import CLIPTextModel | |
return CLIPTextModel | |
elif model_class == "RobertaSeriesModelWithTransformation": | |
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation | |
return RobertaSeriesModelWithTransformation | |
else: | |
raise ValueError(f"{model_class} is not supported.") | |
def parse_args(input_args=None): | |
parser = argparse.ArgumentParser(description="Simple example of a training script.") | |
parser.add_argument( | |
"--pretrained_model_name_or_path", | |
type=str, | |
default=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( | |
"--instance_data_dir", | |
type=str, | |
default=None, | |
required=True, | |
help="A folder containing the training data of instance images.", | |
) | |
parser.add_argument( | |
"--class_data_dir", | |
type=str, | |
default=None, | |
required=False, | |
help="A folder containing the training data of class images.", | |
) | |
parser.add_argument( | |
"--instance_prompt", | |
type=str, | |
default=None, | |
required=True, | |
help="The prompt with identifier specifying the instance", | |
) | |
parser.add_argument( | |
"--class_prompt", | |
type=str, | |
default=None, | |
help="The prompt to specify images in the same class as provided instance images.", | |
) | |
parser.add_argument( | |
"--with_prior_preservation", | |
default=False, | |
action="store_true", | |
help="Flag to add prior preservation loss.", | |
) | |
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") | |
parser.add_argument( | |
"--num_class_images", | |
type=int, | |
default=100, | |
help=( | |
"Minimal class images for prior preservation loss. If there are not enough images already present in" | |
" class_data_dir, additional images will be sampled with class_prompt." | |
), | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="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", | |
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") | |
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. 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=5e-6, | |
help="Initial learning rate (after the potential warmup period) to use.", | |
) | |
parser.add_argument( | |
"--scale_lr", | |
action="store_true", | |
default=False, | |
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", | |
) | |
parser.add_argument( | |
"--lr_scheduler", | |
type=str, | |
default="constant", | |
help=( | |
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' | |
' "constant", "constant_with_warmup"]' | |
), | |
) | |
parser.add_argument( | |
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." | |
) | |
parser.add_argument( | |
"--lr_num_cycles", | |
type=int, | |
default=1, | |
help="Number of hard resets of the lr in cosine_with_restarts scheduler.", | |
) | |
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") | |
parser.add_argument( | |
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." | |
) | |
parser.add_argument("--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." | |
) | |
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 and the tokenizes prompts. | |
""" | |
def __init__( | |
self, | |
instance_data_root, | |
instance_prompt, | |
tokenizer, | |
class_data_root=None, | |
class_prompt=None, | |
size=512, | |
center_crop=False, | |
): | |
self.size = size | |
self.center_crop = center_crop | |
self.tokenizer = tokenizer | |
self.instance_data_root = [] | |
self.instance_images_path = [] | |
self.num_instance_images = [] | |
self.instance_prompt = [] | |
self.class_data_root = [] | |
self.class_images_path = [] | |
self.num_class_images = [] | |
self.class_prompt = [] | |
self._length = 0 | |
for i in range(len(instance_data_root)): | |
self.instance_data_root.append(Path(instance_data_root[i])) | |
if not self.instance_data_root[i].exists(): | |
raise ValueError("Instance images root doesn't exists.") | |
self.instance_images_path.append(list(Path(instance_data_root[i]).iterdir())) | |
self.num_instance_images.append(len(self.instance_images_path[i])) | |
self.instance_prompt.append(instance_prompt[i]) | |
self._length += self.num_instance_images[i] | |
if class_data_root is not None: | |
self.class_data_root.append(Path(class_data_root[i])) | |
self.class_data_root[i].mkdir(parents=True, exist_ok=True) | |
self.class_images_path.append(list(self.class_data_root[i].iterdir())) | |
self.num_class_images.append(len(self.class_images_path)) | |
if self.num_class_images[i] > self.num_instance_images[i]: | |
self._length -= self.num_instance_images[i] | |
self._length += self.num_class_images[i] | |
self.class_prompt.append(class_prompt[i]) | |
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 = {} | |
for i in range(len(self.instance_images_path)): | |
instance_image = Image.open(self.instance_images_path[i][index % self.num_instance_images[i]]) | |
if not instance_image.mode == "RGB": | |
instance_image = instance_image.convert("RGB") | |
example[f"instance_images_{i}"] = self.image_transforms(instance_image) | |
example[f"instance_prompt_ids_{i}"] = self.tokenizer( | |
self.instance_prompt[i], | |
truncation=True, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
return_tensors="pt", | |
).input_ids | |
if self.class_data_root: | |
for i in range(len(self.class_data_root)): | |
class_image = Image.open(self.class_images_path[i][index % self.num_class_images[i]]) | |
if not class_image.mode == "RGB": | |
class_image = class_image.convert("RGB") | |
example[f"class_images_{i}"] = self.image_transforms(class_image) | |
example[f"class_prompt_ids_{i}"] = self.tokenizer( | |
self.class_prompt[i], | |
truncation=True, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
return_tensors="pt", | |
).input_ids | |
return example | |
def collate_fn(num_instances, examples, with_prior_preservation=False): | |
input_ids = [] | |
pixel_values = [] | |
for i in range(num_instances): | |
input_ids += [example[f"instance_prompt_ids_{i}"] for example in examples] | |
pixel_values += [example[f"instance_images_{i}"] 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: | |
for i in range(num_instances): | |
input_ids += [example[f"class_prompt_ids_{i}"] for example in examples] | |
pixel_values += [example[f"class_images_{i}"] for example in examples] | |
pixel_values = torch.stack(pixel_values) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
input_ids = torch.cat(input_ids, dim=0) | |
batch = { | |
"input_ids": input_ids, | |
"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 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, | |
logging_dir=logging_dir, | |
project_config=accelerator_project_config, | |
) | |
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate | |
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. | |
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. | |
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: | |
raise ValueError( | |
"Gradient accumulation is not supported when training the text encoder in distributed training. " | |
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future." | |
) | |
# Parse instance and class inputs, and double check that lengths match | |
instance_data_dir = args.instance_data_dir.split(",") | |
instance_prompt = args.instance_prompt.split(",") | |
assert all( | |
x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)] | |
), "Instance data dir and prompt inputs are not of the same length." | |
if args.with_prior_preservation: | |
class_data_dir = args.class_data_dir.split(",") | |
class_prompt = args.class_prompt.split(",") | |
assert all( | |
x == len(instance_data_dir) | |
for x in [len(instance_data_dir), len(instance_prompt), len(class_data_dir), len(class_prompt)] | |
), "Instance & class data dir or prompt inputs are not of the same length." | |
else: | |
class_data_dir = args.class_data_dir | |
class_prompt = args.class_prompt | |
# 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) | |
# Generate class images if prior preservation is enabled. | |
if args.with_prior_preservation: | |
for i in range(len(class_data_dir)): | |
class_images_dir = Path(class_data_dir[i]) | |
if not class_images_dir.exists(): | |
class_images_dir.mkdir(parents=True) | |
cur_class_images = len(list(class_images_dir.iterdir())) | |
if cur_class_images < args.num_class_images: | |
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 | |
if args.prior_generation_precision == "fp32": | |
torch_dtype = torch.float32 | |
elif args.prior_generation_precision == "fp16": | |
torch_dtype = torch.float16 | |
elif args.prior_generation_precision == "bf16": | |
torch_dtype = torch.bfloat16 | |
pipeline = DiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
torch_dtype=torch_dtype, | |
safety_checker=None, | |
revision=args.revision, | |
) | |
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(class_prompt[i], 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, token=args.hub_token | |
).repo_id | |
# Load the tokenizer | |
if args.tokenizer_name: | |
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False) | |
elif args.pretrained_model_name_or_path: | |
tokenizer = AutoTokenizer.from_pretrained( | |
args.pretrained_model_name_or_path, | |
subfolder="tokenizer", | |
revision=args.revision, | |
use_fast=False, | |
) | |
# import correct text encoder class | |
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) | |
# Load scheduler and models | |
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") | |
text_encoder = text_encoder_cls.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision | |
) | |
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 not args.train_text_encoder: | |
text_encoder.requires_grad_(False) | |
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") | |
if args.gradient_checkpointing: | |
unet.enable_gradient_checkpointing() | |
if args.train_text_encoder: | |
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 | |
) | |
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs | |
if args.use_8bit_adam: | |
try: | |
import bitsandbytes as bnb | |
except ImportError: | |
raise ImportError( | |
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." | |
) | |
optimizer_class = bnb.optim.AdamW8bit | |
else: | |
optimizer_class = torch.optim.AdamW | |
# Optimizer creation | |
params_to_optimize = ( | |
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() | |
) | |
optimizer = optimizer_class( | |
params_to_optimize, | |
lr=args.learning_rate, | |
betas=(args.adam_beta1, args.adam_beta2), | |
weight_decay=args.adam_weight_decay, | |
eps=args.adam_epsilon, | |
) | |
# Dataset and DataLoaders creation: | |
train_dataset = DreamBoothDataset( | |
instance_data_root=instance_data_dir, | |
instance_prompt=instance_prompt, | |
class_data_root=class_data_dir if args.with_prior_preservation else None, | |
class_prompt=class_prompt, | |
tokenizer=tokenizer, | |
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(len(instance_data_dir), examples, args.with_prior_preservation), | |
num_workers=1, | |
) | |
# 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, | |
num_cycles=args.lr_num_cycles, | |
power=args.lr_power, | |
) | |
# Prepare everything with our `accelerator`. | |
if args.train_text_encoder: | |
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, text_encoder, optimizer, train_dataloader, lr_scheduler | |
) | |
else: | |
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, optimizer, train_dataloader, lr_scheduler | |
) | |
# For mixed precision training we cast 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 text_encoder to device and cast to weight_dtype | |
vae.to(accelerator.device, dtype=weight_dtype) | |
if not args.train_text_encoder: | |
text_encoder.to(accelerator.device, dtype=weight_dtype) | |
# We need to recalculate our total training steps as the size of the training dataloader may have changed. | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if overrode_max_train_steps: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
# Afterwards we recalculate our number of training epochs | |
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
# We need to initialize the trackers we use, and also store our configuration. | |
# The trackers initializes automatically on the main process. | |
if accelerator.is_main_process: | |
accelerator.init_trackers("dreambooth", 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 | |
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): | |
unet.train() | |
if args.train_text_encoder: | |
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(unet): | |
# 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] | |
# 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}") | |
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.parameters(), text_encoder.parameters()) | |
if args.train_text_encoder | |
else unet.parameters() | |
) | |
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad() | |
# 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: | |
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: | |
pipeline = DiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
unet=accelerator.unwrap_model(unet), | |
text_encoder=accelerator.unwrap_model(text_encoder), | |
revision=args.revision, | |
) | |
pipeline.save_pretrained(args.output_dir) | |
if args.push_to_hub: | |
upload_folder( | |
repo_id=repo_id, | |
folder_path=args.output_dir, | |
commit_message="End of training", | |
ignore_patterns=["step_*", "epoch_*"], | |
) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
args = parse_args() | |
main(args) | |