# ported from https://github.com/huggingface/diffusers/tree/a6fb9407fd45e76ccd47d13f08f0dd835967d620/examples/research_projects/dreambooth_inpaint
import argparse
import hashlib
import itertools
import math
import os
import random
from pathlib import Path
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 ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
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
from diffusers import (
AutoencoderKL,
DDPMScheduler,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
# 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 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",
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(
"--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")
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 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.'
),
)
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_resize_and_crop = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
]
)
self.image_transforms = transforms.Compose(
[
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")
instance_image = self.image_transforms_resize_and_crop(instance_image)
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")
class_image = self.image_transforms_resize_and_crop(class_image)
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 main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
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="tensorboard",
logging_dir=logging_dir,
project_config=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."
)
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.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 = 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_pretrained(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):
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)
# 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)
# 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
)
accelerator.register_for_checkpointing(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}")
global_step = 0
first_epoch = 0
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] 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()
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
# 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 * vae.config.scaling_factor
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
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
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:
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__":
main()