dreambooth-altdiffusion / train_dreambooth.py
root
update ui
0ea1b17
import argparse
import itertools
import math
import os
from pathlib import Path
from typing import Optional
import subprocess
import sys
import gc
import random
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, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
logger = get_logger(__name__)
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
)
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():
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="",
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="",
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="fp16",
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(
"--save_n_steps",
type=int,
default=1,
help=("Save the model every n global_steps"),
)
parser.add_argument(
"--save_starting_step",
type=int,
default=1,
help=("The step from which it starts saving intermediary checkpoints"),
)
parser.add_argument(
"--stop_text_encoder_training",
type=int,
default=1000000,
help=("The step at which the text_encoder is no longer trained"),
)
parser.add_argument(
"--image_captions_filename",
action="store_true",
help="Get captions from filename",
)
parser.add_argument(
"--dump_only_text_encoder",
action="store_true",
default=False,
help="Dump only text encoder",
)
parser.add_argument(
"--train_only_unet",
action="store_true",
default=False,
help="Train only the unet",
)
parser.add_argument(
"--cache_latents",
action="store_true",
default=False,
help="Train only the unet",
)
parser.add_argument(
"--Session_dir",
type=str,
default="",
help="Current session directory",
)
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,
args,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.image_captions_filename = None
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 args.image_captions_filename:
self.image_captions_filename = True
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())
random.shuffle(self.class_images_path)
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 = {}
path = self.instance_images_path[index % self.num_instance_images]
instance_image = Image.open(path)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_prompt = self.instance_prompt
if self.image_captions_filename:
filename = Path(path).stem
pt=''.join([i for i in filename if not i.isdigit()])
pt=pt.replace("_"," ")
pt=pt.replace("(","")
pt=pt.replace(")","")
pt=pt.replace("-","")
instance_prompt = pt
sys.stdout.write(" " +instance_prompt+" ")
sys.stdout.flush()
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
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_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
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]
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 merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
"""
Starts from base starting dict and then adds the remaining key values from updater replacing the values from
the first starting/base dict with the second updater dict.
For later: how does d = {**d1, **d2} replace collision?
:param starting_dict:
:param updater_dict:
:return:
"""
new_dict: dict = starting_dict.copy() # start with keys and values of starting_dict
new_dict.update(updater_dict) # modifies starting_dict with keys and values of updater_dict
return new_dict
def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
"""
ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
:param args1:
:param args2:
:return:
"""
# - the merged args
# The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
args = argparse.Namespace(**merged_key_values_for_namespace)
return args
def run_training(args_imported):
args_default = parse_args()
args = merge_args(args_default, args_imported)
print(args)
logging_dir = Path(args.output_dir, args.logging_dir)
i=args.save_starting_step
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 = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, torch_dtype=torch_dtype
)
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)
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
):
with torch.autocast("cuda"):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
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 = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer", use_fast=False)
# support for Altdiffusion
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
# Load models and create wrapper for stable diffusion
if args.train_only_unet:
if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
# text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
text_encoder = text_encoder_cls.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
elif os.path.exists(str(args.output_dir+"/text_encoder")):
# text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
text_encoder = text_encoder_cls.from_pretrained(args.output_dir, subfolder="text_encoder")
else:
# text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
text_encoder = text_encoder_cls.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
else:
# text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
text_encoder = text_encoder_cls.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,
args=args,
)
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
)
# 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)
if args.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()
# 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))
def bar(prg):
br='|'+'█' * prg + ' ' * (25-prg)+'|'
return br
# 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)
global_step = 0
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):
import pdb
pdb.set_trace()
with accelerator.accumulate(unet):
# Convert images to latent space
with torch.no_grad():
if args.cache_latents:
latents_dist = batch[0][0]
else:
latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
latents = latents_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
if(args.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="none").mean([1, 2, 3]).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
fll=round((global_step*100)/args.max_train_steps)
fll=round(fll/4)
pr=bar(fll)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
progress_bar.set_description_str("Progress:"+pr)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
if accelerator.is_main_process:
print(" " +" Freezing the text_encoder ..."+" ")
frz_dir=args.output_dir + "/text_encoder_frozen"
if os.path.exists(frz_dir):
subprocess.call('rm -r '+ frz_dir, shell=True)
os.mkdir(frz_dir)
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
pipeline.text_encoder.save_pretrained(frz_dir)
if args.save_n_steps >= 200:
if global_step < args.max_train_steps and global_step+1==i:
ckpt_name = "_step_" + str(global_step+1)
save_dir = Path(args.output_dir+ckpt_name)
save_dir=str(save_dir)
save_dir=save_dir.replace(" ", "_")
if not os.path.exists(save_dir):
os.mkdir(save_dir)
inst=save_dir[16:]
inst=inst.replace(" ", "_")
print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
# Create the pipeline using the trained modules and save it.
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),
)
pipeline.save_pretrained(save_dir)
frz_dir=args.output_dir + "/text_encoder_frozen"
if args.train_text_encoder and os.path.exists(frz_dir):
subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)
chkpth=args.Session_dir+"/"+inst+".ckpt"
subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
subprocess.call('rm -r '+ save_dir, shell=True)
i=i+args.save_n_steps
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
if args.dump_only_text_encoder:
txt_dir=args.output_dir + "/text_encoder_trained"
if not os.path.exists(txt_dir):
os.mkdir(txt_dir)
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
pipeline.text_encoder.save_pretrained(txt_dir)
elif args.train_only_unet:
pipeline = DiffusionPipeline.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)
txt_dir=args.output_dir + "/text_encoder_trained"
subprocess.call('rm -r '+txt_dir, shell=True)
else:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
frz_dir=args.output_dir + "/text_encoder_frozen"
pipeline.save_pretrained(args.output_dir)
if args.train_text_encoder and os.path.exists(frz_dir):
subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
subprocess.call('rm -r '+ frz_dir, shell=True)
if args.push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()
del pipeline
torch.cuda.empty_cache()
gc.collect()
if __name__ == "__main__":
pass
#main()