mirror of https://github.com/hpcaitech/ColossalAI
721 lines
30 KiB
Python
721 lines
30 KiB
Python
import argparse
|
|
import hashlib
|
|
import itertools
|
|
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 accelerate import Accelerator
|
|
from accelerate.logging import get_logger
|
|
from accelerate.utils import set_seed
|
|
from diffusers import (
|
|
AutoencoderKL,
|
|
DDPMScheduler,
|
|
StableDiffusionInpaintPipeline,
|
|
StableDiffusionPipeline,
|
|
UNet2DConditionModel,
|
|
)
|
|
from diffusers.optimization import get_scheduler
|
|
from huggingface_hub import HfFolder, Repository, whoami
|
|
from PIL import Image, ImageDraw
|
|
from torch.utils.data import Dataset
|
|
from torchvision import transforms
|
|
from tqdm.auto import tqdm
|
|
from transformers import CLIPTextModel, CLIPTokenizer
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
def prepare_mask_and_masked_image(image, mask):
|
|
image = np.array(image.convert("RGB"))
|
|
image = image[None].transpose(0, 3, 1, 2)
|
|
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
|
|
|
mask = np.array(mask.convert("L"))
|
|
mask = mask.astype(np.float32) / 255.0
|
|
mask = mask[None, None]
|
|
mask[mask < 0.5] = 0
|
|
mask[mask >= 0.5] = 1
|
|
mask = torch.from_numpy(mask)
|
|
|
|
masked_image = image * (mask < 0.5)
|
|
|
|
return mask, masked_image
|
|
|
|
|
|
# generate random masks
|
|
def random_mask(im_shape, ratio=1, mask_full_image=False):
|
|
mask = Image.new("L", im_shape, 0)
|
|
draw = ImageDraw.Draw(mask)
|
|
size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))
|
|
# use this to always mask the whole image
|
|
if mask_full_image:
|
|
size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))
|
|
limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)
|
|
center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))
|
|
draw_type = random.randint(0, 1)
|
|
if draw_type == 0 or mask_full_image:
|
|
draw.rectangle(
|
|
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
|
|
fill=255,
|
|
)
|
|
else:
|
|
draw.ellipse(
|
|
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
|
|
fill=255,
|
|
)
|
|
|
|
return mask
|
|
|
|
|
|
def parse_args():
|
|
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(
|
|
"--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,
|
|
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 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(
|
|
"--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
|
|
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.instance_data_dir is None:
|
|
raise ValueError("You must specify a train data directory.")
|
|
|
|
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.")
|
|
|
|
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 = 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.instance_prompt = instance_prompt
|
|
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())
|
|
self.num_class_images = len(self.class_images_path)
|
|
self._length = max(self.num_class_images, self.num_instance_images)
|
|
self.class_prompt = class_prompt
|
|
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])
|
|
if not instance_image.mode == "RGB":
|
|
instance_image = instance_image.convert("RGB")
|
|
|
|
example["PIL_images"] = instance_image
|
|
example["instance_images"] = self.image_transforms(instance_image)
|
|
|
|
example["instance_prompt_ids"] = self.tokenizer(
|
|
self.instance_prompt,
|
|
padding="do_not_pad",
|
|
truncation=True,
|
|
max_length=self.tokenizer.model_max_length,
|
|
).input_ids
|
|
|
|
if self.class_data_root:
|
|
class_image = Image.open(self.class_images_path[index % self.num_class_images])
|
|
if not class_image.mode == "RGB":
|
|
class_image = class_image.convert("RGB")
|
|
example["class_images"] = self.image_transforms(class_image)
|
|
example["class_PIL_images"] = class_image
|
|
example["class_prompt_ids"] = self.tokenizer(
|
|
self.class_prompt,
|
|
padding="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
|
|
|
|
|
|
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 = parse_args()
|
|
logging_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with="tensorboard",
|
|
logging_dir=logging_dir,
|
|
)
|
|
|
|
# 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.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
|
|
pipeline = StableDiffusionInpaintPipeline.from_pretrained(args.pretrained_model_name_or_path,
|
|
torch_dtype=torch_dtype,
|
|
safety_checker=None)
|
|
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,
|
|
num_workers=1)
|
|
|
|
sample_dataloader = accelerator.prepare(sample_dataloader)
|
|
pipeline.to(accelerator.device)
|
|
transform_to_pil = transforms.ToPILImage()
|
|
for example in tqdm(sample_dataloader,
|
|
desc="Generating class images",
|
|
disable=not accelerator.is_local_main_process):
|
|
bsz = len(example["prompt"])
|
|
fake_images = torch.rand((3, args.resolution, args.resolution))
|
|
transform_to_pil = transforms.ToPILImage()
|
|
fake_pil_images = transform_to_pil(fake_images)
|
|
|
|
fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)
|
|
|
|
images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).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.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
|
|
repo = Repository(args.output_dir, clone_from=repo_name)
|
|
|
|
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 the tokenizer
|
|
if args.tokenizer_name:
|
|
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
|
|
elif args.pretrained_model_name_or_path:
|
|
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
|
|
|
|
# Load models and create wrapper for stable diffusion
|
|
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
|
|
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
|
|
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
|
|
|
|
vae.requires_grad_(False)
|
|
if not args.train_text_encoder:
|
|
text_encoder.requires_grad_(False)
|
|
|
|
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(
|
|
instance_data_root=args.instance_data_dir,
|
|
instance_prompt=args.instance_prompt,
|
|
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
|
|
class_prompt=args.class_prompt,
|
|
tokenizer=tokenizer,
|
|
size=args.resolution,
|
|
center_crop=args.center_crop,
|
|
)
|
|
|
|
def collate_fn(examples):
|
|
image_transforms = transforms.Compose([
|
|
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
|
|
])
|
|
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]
|
|
pior_pil = [example["class_PIL_images"] for example in examples]
|
|
|
|
masks = []
|
|
masked_images = []
|
|
for example in examples:
|
|
pil_image = example["PIL_images"]
|
|
# generate a random mask
|
|
mask = random_mask(pil_image.size, 1, False)
|
|
# apply transforms
|
|
mask = image_transforms(mask)
|
|
pil_image = image_transforms(pil_image)
|
|
# prepare mask and masked image
|
|
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
|
|
|
|
masks.append(mask)
|
|
masked_images.append(masked_image)
|
|
|
|
if args.with_prior_preservation:
|
|
for pil_image in pior_pil:
|
|
# generate a random mask
|
|
mask = random_mask(pil_image.size, 1, False)
|
|
# apply transforms
|
|
mask = image_transforms(mask)
|
|
pil_image = image_transforms(pil_image)
|
|
# prepare mask and masked image
|
|
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
|
|
|
|
masks.append(mask)
|
|
masked_images.append(masked_image)
|
|
|
|
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
|
|
masks = torch.stack(masks)
|
|
masked_images = torch.stack(masked_images)
|
|
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images}
|
|
return batch
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(train_dataset,
|
|
batch_size=args.train_batch_size,
|
|
shuffle=True,
|
|
collate_fn=collate_fn)
|
|
|
|
# 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)
|
|
|
|
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)
|
|
|
|
# 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}")
|
|
# 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
|
|
|
|
for epoch in range(args.num_train_epochs):
|
|
unet.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet):
|
|
# Convert images to latent space
|
|
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * 0.18215
|
|
|
|
# Convert masked images to latent space
|
|
masked_latents = vae.encode(batch["masked_images"].reshape(
|
|
batch["pixel_values"].shape).to(dtype=weight_dtype)).latent_dist.sample()
|
|
masked_latents = masked_latents * 0.18215
|
|
|
|
masks = batch["masks"]
|
|
# resize the mask to latents shape as we concatenate the mask to the latents
|
|
mask = torch.stack([
|
|
torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
|
|
for mask in masks
|
|
])
|
|
mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)
|
|
|
|
# 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)
|
|
|
|
# concatenate the noised latents with the mask and the masked latents
|
|
latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
|
|
|
# Predict the noise residual
|
|
noise_pred = unet(latent_model_input, 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 noise_pred into two parts and compute the loss on each part separately.
|
|
noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
|
|
target, target_prior = torch.chunk(target, 2, dim=0)
|
|
|
|
# Compute instance loss
|
|
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
|
|
|
|
# Compute prior loss
|
|
prior_loss = F.mse_loss(noise_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(noise_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
|
|
|
|
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
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
# Create the pipeline using using the trained modules and save it.
|
|
if accelerator.is_main_process:
|
|
pipeline = StableDiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
unet=accelerator.unwrap_model(unet),
|
|
text_encoder=accelerator.unwrap_model(text_encoder),
|
|
)
|
|
pipeline.save_pretrained(args.output_dir)
|
|
|
|
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__":
|
|
main()
|