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dreambooth_fixed.py
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import argparse | |
import hashlib | |
import itertools | |
import random | |
import json | |
import logging | |
import math | |
import os | |
from contextlib import nullcontext | |
from pathlib import Path | |
from typing import Optional | |
import torch | |
import torch.nn.functional as F | |
import torch.utils.checkpoint | |
from torch.utils.data import Dataset | |
from accelerate import Accelerator | |
from accelerate.logging import get_logger | |
from accelerate.utils import set_seed | |
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel | |
from diffusers.optimization import get_scheduler | |
from diffusers.utils.import_utils import is_xformers_available | |
from huggingface_hub import HfFolder, Repository, whoami | |
from PIL import Image | |
from torchvision import transforms | |
from tqdm.auto import tqdm | |
from transformers import CLIPTextModel, CLIPTokenizer | |
torch.backends.cudnn.benchmark = True | |
logger = get_logger(__name__) | |
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_name_or_path", | |
type=str, | |
default=None, | |
help="Path to pretrained vae or vae 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, | |
help="A folder containing the training data of instance images.", | |
) | |
parser.add_argument( | |
"--class_data_dir", | |
type=str, | |
default=None, | |
help="A folder containing the training data of class images.", | |
) | |
parser.add_argument( | |
"--instance_prompt", | |
type=str, | |
default=None, | |
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( | |
"--save_sample_prompt", | |
type=str, | |
default=None, | |
help="The prompt used to generate sample outputs to save.", | |
) | |
parser.add_argument( | |
"--save_sample_negative_prompt", | |
type=str, | |
default=None, | |
help="The negative prompt used to generate sample outputs to save.", | |
) | |
parser.add_argument( | |
"--n_save_sample", | |
type=int, | |
default=4, | |
help="The number of samples to save.", | |
) | |
parser.add_argument( | |
"--save_guidance_scale", | |
type=float, | |
default=7.5, | |
help="CFG for save sample.", | |
) | |
parser.add_argument( | |
"--save_infer_steps", | |
type=int, | |
default=20, | |
help="The number of inference steps for save sample.", | |
) | |
parser.add_argument( | |
"--pad_tokens", | |
default=False, | |
action="store_true", | |
help="Flag to pad tokens to length 77.", | |
) | |
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 not have enough images, 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", action="store_true", help="Whether to center crop images before resizing to resolution" | |
) | |
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( | |
"--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( | |
"--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( | |
"--project_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("--log_interval", type=int, default=10, help="Log every N steps.") | |
parser.add_argument("--save_interval", type=int, default=10_000, help="Save weights every N steps.") | |
parser.add_argument("--save_min_steps", type=int, default=0, help="Start saving weights after N steps.") | |
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("--not_cache_latents", action="store_true", help="Do not precompute and cache latents from VAE.") | |
parser.add_argument("--hflip", action="store_true", help="Apply horizontal flip data augmentation.") | |
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") | |
parser.add_argument( | |
"--concepts_list", | |
type=str, | |
default=None, | |
help="Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.", | |
) | |
parser.add_argument( | |
"--read_prompts_from_txts", | |
action="store_true", | |
help="Use prompt per image. Put prompts in the same directory as images, e.g. for image.png create image.png.txt.", | |
) | |
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 | |
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, | |
concepts_list, | |
tokenizer, | |
with_prior_preservation=True, | |
size=512, | |
center_crop=False, | |
num_class_images=None, | |
pad_tokens=False, | |
hflip=False, | |
read_prompts_from_txts=False, | |
): | |
self.size = size | |
self.center_crop = center_crop | |
self.tokenizer = tokenizer | |
self.with_prior_preservation = with_prior_preservation | |
self.pad_tokens = pad_tokens | |
self.read_prompts_from_txts = read_prompts_from_txts | |
self.instance_images_path = [] | |
self.class_images_path = [] | |
for concept in concepts_list: | |
inst_img_path = [ | |
(x, concept["instance_prompt"]) | |
for x in Path(concept["instance_data_dir"]).iterdir() | |
if x.is_file() and not str(x).endswith(".txt") | |
] | |
self.instance_images_path.extend(inst_img_path) | |
if with_prior_preservation: | |
class_img_path = [(x, concept["class_prompt"]) for x in Path(concept["class_data_dir"]).iterdir() if x.is_file()] | |
self.class_images_path.extend(class_img_path[:num_class_images]) | |
random.shuffle(self.instance_images_path) | |
self.num_instance_images = len(self.instance_images_path) | |
self.num_class_images = len(self.class_images_path) | |
self._length = max(self.num_class_images, self.num_instance_images) | |
self.image_transforms = transforms.Compose( | |
[ | |
transforms.RandomHorizontalFlip(0.5 * hflip), | |
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_path, instance_prompt = self.instance_images_path[index % self.num_instance_images] | |
if self.read_prompts_from_txts: | |
with open(str(instance_path) + ".txt") as f: | |
instance_prompt = f.read().strip() | |
instance_image = Image.open(instance_path) | |
if not instance_image.mode == "RGB": | |
instance_image = instance_image.convert("RGB") | |
example["instance_images"] = self.image_transforms(instance_image) | |
example["instance_prompt_ids"] = self.tokenizer( | |
instance_prompt, | |
padding="max_length" if self.pad_tokens else "do_not_pad", | |
truncation=True, | |
max_length=self.tokenizer.model_max_length, | |
).input_ids | |
if self.with_prior_preservation: | |
class_path, class_prompt = self.class_images_path[index % self.num_class_images] | |
class_image = Image.open(class_path) | |
if not class_image.mode == "RGB": | |
class_image = class_image.convert("RGB") | |
example["class_images"] = self.image_transforms(class_image) | |
example["class_prompt_ids"] = self.tokenizer( | |
class_prompt, | |
padding="max_length" if self.pad_tokens else "do_not_pad", | |
truncation=True, | |
max_length=self.tokenizer.model_max_length, | |
).input_ids | |
return example | |
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 | |
class LatentsDataset(Dataset): | |
def __init__(self, latents_cache, text_encoder_cache): | |
self.latents_cache = latents_cache | |
self.text_encoder_cache = text_encoder_cache | |
def __len__(self): | |
return len(self.latents_cache) | |
def __getitem__(self, index): | |
return self.latents_cache[index], self.text_encoder_cache[index] | |
class AverageMeter: | |
def __init__(self, name=None): | |
self.name = name | |
self.reset() | |
def reset(self): | |
self.sum = self.count = self.avg = 0 | |
def update(self, val, n=1): | |
self.sum += val * n | |
self.count += n | |
self.avg = self.sum / self.count | |
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(args): | |
project_dir = Path(args.output_dir, "0", args.project_dir) | |
accelerator = Accelerator( | |
gradient_accumulation_steps=args.gradient_accumulation_steps, | |
mixed_precision=args.mixed_precision, | |
log_with="tensorboard", | |
project_dir=project_dir, | |
) | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
level=logging.INFO, | |
) | |
# 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." | |
) | |
if args.seed is not None: | |
set_seed(args.seed) | |
if args.concepts_list is None: | |
args.concepts_list = [ | |
{ | |
"instance_prompt": args.instance_prompt, | |
"class_prompt": args.class_prompt, | |
"instance_data_dir": args.instance_data_dir, | |
"class_data_dir": args.class_data_dir | |
} | |
] | |
else: | |
with open(args.concepts_list, "r") as f: | |
args.concepts_list = json.load(f) | |
if args.with_prior_preservation: | |
pipeline = None | |
for concept in args.concepts_list: | |
class_images_dir = Path(concept["class_data_dir"]) | |
class_images_dir.mkdir(parents=True, exist_ok=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 pipeline is None: | |
pipeline = StableDiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
vae=AutoencoderKL.from_pretrained( | |
args.pretrained_vae_name_or_path or args.pretrained_model_name_or_path, | |
subfolder=None if args.pretrained_vae_name_or_path else "vae", | |
revision=None if args.pretrained_vae_name_or_path else args.revision, | |
torch_dtype=torch_dtype | |
), | |
torch_dtype=torch_dtype, | |
safety_checker=None, | |
revision=args.revision | |
) | |
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) | |
if is_xformers_available(): | |
pipeline.enable_xformers_memory_efficient_attention() | |
pipeline.set_progress_bar_config(disable=True) | |
pipeline.to(accelerator.device) | |
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(concept["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) | |
with torch.autocast("cuda"), torch.inference_mode(): | |
for example in tqdm( | |
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process | |
): | |
images = pipeline( | |
example["prompt"], | |
num_inference_steps=args.save_infer_steps | |
).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() | |
# Load the tokenizer | |
if args.tokenizer_name: | |
tokenizer = CLIPTokenizer.from_pretrained( | |
args.tokenizer_name, | |
revision=args.revision, | |
) | |
elif args.pretrained_model_name_or_path: | |
tokenizer = CLIPTokenizer.from_pretrained( | |
args.pretrained_model_name_or_path, | |
subfolder="tokenizer", | |
revision=args.revision, | |
) | |
# Load models and create wrapper for stable diffusion | |
text_encoder = CLIPTextModel.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, | |
torch_dtype=torch.float32 | |
) | |
vae.requires_grad_(False) | |
if not args.train_text_encoder: | |
text_encoder.requires_grad_(False) | |
if is_xformers_available(): | |
vae.enable_xformers_memory_efficient_attention() | |
unet.enable_xformers_memory_efficient_attention() | |
else: | |
logger.warning("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() | |
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 | |
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, | |
) | |
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler") | |
train_dataset = DreamBoothDataset( | |
concepts_list=args.concepts_list, | |
tokenizer=tokenizer, | |
with_prior_preservation=args.with_prior_preservation, | |
size=args.resolution, | |
center_crop=args.center_crop, | |
num_class_images=args.num_class_images, | |
pad_tokens=args.pad_tokens, | |
hflip=args.hflip, | |
read_prompts_from_txts=args.read_prompts_from_txts, | |
) | |
def collate_fn(examples): | |
input_ids = [example["instance_prompt_ids"] for example in examples] | |
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 args.with_prior_preservation: | |
input_ids += [example["class_prompt_ids"] for example in examples] | |
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() | |
input_ids = tokenizer.pad( | |
{"input_ids": input_ids}, | |
padding=True, | |
return_tensors="pt", | |
).input_ids | |
batch = { | |
"input_ids": input_ids, | |
"pixel_values": pixel_values, | |
} | |
return batch | |
train_dataloader = torch.utils.data.DataLoader( | |
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, pin_memory=True | |
) | |
weight_dtype = torch.float32 | |
if args.mixed_precision == "fp16": | |
weight_dtype = torch.float16 | |
elif args.mixed_precision == "bf16": | |
weight_dtype = torch.bfloat16 | |
# Move text_encode and vae to gpu. | |
# 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. | |
vae.to(accelerator.device, dtype=weight_dtype) | |
if not args.train_text_encoder: | |
text_encoder.to(accelerator.device, dtype=weight_dtype) | |
if not args.not_cache_latents: | |
latents_cache = [] | |
text_encoder_cache = [] | |
for batch in tqdm(train_dataloader, desc="Caching latents"): | |
with torch.no_grad(): | |
batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype) | |
batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True) | |
latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist) | |
if args.train_text_encoder: | |
text_encoder_cache.append(batch["input_ids"]) | |
else: | |
text_encoder_cache.append(text_encoder(batch["input_ids"])[0]) | |
train_dataset = LatentsDataset(latents_cache, text_encoder_cache) | |
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True) | |
del vae | |
if not args.train_text_encoder: | |
del text_encoder | |
if torch.cuda.is_available(): | |
torch.cuda.empty_cache() | |
# 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, | |
) | |
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 | |
) | |
# 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") | |
# 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}") | |
def save_weights(step): | |
# Create the pipeline using using the trained modules and save it. | |
if accelerator.is_main_process: | |
if args.train_text_encoder: | |
text_enc_model = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True) | |
else: | |
text_enc_model = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision) | |
pipeline = StableDiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
unet=accelerator.unwrap_model(unet, keep_fp32_wrapper=True), | |
text_encoder=text_enc_model, | |
vae=AutoencoderKL.from_pretrained( | |
args.pretrained_vae_name_or_path or args.pretrained_model_name_or_path, | |
subfolder=None if args.pretrained_vae_name_or_path else "vae", | |
revision=None if args.pretrained_vae_name_or_path else args.revision, | |
), | |
safety_checker=None, | |
torch_dtype=torch.float16, | |
revision=args.revision, | |
) | |
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) | |
if is_xformers_available(): | |
pipeline.enable_xformers_memory_efficient_attention() | |
save_dir = os.path.join(args.output_dir, f"{step}") | |
pipeline.save_pretrained(save_dir) | |
with open(os.path.join(save_dir, "args.json"), "w") as f: | |
json.dump(args.__dict__, f, indent=2) | |
if args.save_sample_prompt is not None: | |
pipeline = pipeline.to(accelerator.device) | |
g_cuda = torch.Generator(device=accelerator.device).manual_seed(args.seed) | |
pipeline.set_progress_bar_config(disable=True) | |
sample_dir = os.path.join(save_dir, "samples") | |
os.makedirs(sample_dir, exist_ok=True) | |
with torch.autocast("cuda"), torch.inference_mode(): | |
for i in tqdm(range(args.n_save_sample), desc="Generating samples"): | |
images = pipeline( | |
args.save_sample_prompt, | |
negative_prompt=args.save_sample_negative_prompt, | |
guidance_scale=args.save_guidance_scale, | |
num_inference_steps=args.save_infer_steps, | |
generator=g_cuda | |
).images | |
images[0].save(os.path.join(sample_dir, f"{i}.png")) | |
del pipeline | |
if torch.cuda.is_available(): | |
torch.cuda.empty_cache() | |
print(f"[*] Weights saved at {save_dir}") | |
# Only show the progress bar once on each machine. | |
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) | |
progress_bar.set_description("Steps") | |
global_step = 0 | |
loss_avg = AverageMeter() | |
text_enc_context = nullcontext() if args.train_text_encoder else torch.no_grad() | |
for epoch in range(args.num_train_epochs): | |
unet.train() | |
if args.train_text_encoder: | |
text_encoder.train() | |
for step, batch in enumerate(train_dataloader): | |
with accelerator.accumulate(unet): | |
# Convert images to latent space | |
with torch.no_grad(): | |
if not args.not_cache_latents: | |
latent_dist = batch[0][0] | |
else: | |
latent_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist | |
latents = latent_dist.sample() * 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 | |
with text_enc_context: | |
if not args.not_cache_latents: | |
if args.train_text_encoder: | |
encoder_hidden_states = text_encoder(batch[0][1])[0] | |
else: | |
encoder_hidden_states = batch[0][1] | |
else: | |
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(set_to_none=True) | |
loss_avg.update(loss.detach_(), bsz) | |
if not global_step % args.log_interval: | |
logs = {"loss": loss_avg.avg.item(), "lr": lr_scheduler.get_last_lr()[0]} | |
progress_bar.set_postfix(**logs) | |
accelerator.log(logs, step=global_step) | |
if global_step > 0 and not global_step % args.save_interval and global_step >= args.save_min_steps: | |
save_weights(global_step) | |
progress_bar.update(1) | |
global_step += 1 | |
if global_step >= args.max_train_steps: | |
break | |
accelerator.wait_for_everyone() | |
save_weights(global_step) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
args = parse_args() | |
main(args) |
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