# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import copy
import logging
import math
import os
import shutil
from contextlib import nullcontext
from pathlib import Path
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import DataLoader, Dataset, default_collate
from torchvision import transforms
from transformers import (
CLIPTextModelWithProjection,
CLIPTokenizer,
)
import diffusers.optimization
from diffusers import AmusedPipeline, AmusedScheduler, EMAModel, UVit2DModel, VQModel
from diffusers.loaders import AmusedLoraLoaderMixin
from diffusers.utils import is_wandb_available
if is_wandb_available():
import wandb
logger = get_logger(__name__, log_level="INFO")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--instance_data_dataset",
type=str,
default=None,
required=False,
help="A Hugging Face dataset containing the training images",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--instance_data_image", type=str, default=None, required=False, help="A single training image"
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument("--ema_decay", type=float, default=0.9999)
parser.add_argument("--ema_update_after_step", type=int, default=0)
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(
"--output_dir",
type=str,
default="muse_training",
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(
"--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(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
"instructions."
),
)
parser.add_argument(
"--logging_steps",
type=int,
default=50,
)
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 details"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
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(
"--learning_rate",
type=float,
default=0.0003,
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(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
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(
"--report_to",
type=str,
default="wandb",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--validation_prompts", type=str, nargs="*")
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("--split_vae_encode", type=int, required=False, default=None)
parser.add_argument("--min_masking_rate", type=float, default=0.0)
parser.add_argument("--cond_dropout_prob", type=float, default=0.0)
parser.add_argument("--max_grad_norm", default=None, type=float, help="Max gradient norm.", required=False)
parser.add_argument("--use_lora", action="store_true", help="Fine tune the model using LoRa")
parser.add_argument("--text_encoder_use_lora", action="store_true", help="Fine tune the model using LoRa")
parser.add_argument("--lora_r", default=16, type=int)
parser.add_argument("--lora_alpha", default=32, type=int)
parser.add_argument("--lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
parser.add_argument("--text_encoder_lora_r", default=16, type=int)
parser.add_argument("--text_encoder_lora_alpha", default=32, type=int)
parser.add_argument("--text_encoder_lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
parser.add_argument("--train_text_encoder", action="store_true")
parser.add_argument("--image_key", type=str, required=False)
parser.add_argument("--prompt_key", type=str, required=False)
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("--prompt_prefix", type=str, required=False, default=None)
args = parser.parse_args()
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
num_datasources = sum(
[x is not None for x in [args.instance_data_dir, args.instance_data_image, args.instance_data_dataset]]
)
if num_datasources != 1:
raise ValueError(
"provide one and only one of `--instance_data_dir`, `--instance_data_image`, or `--instance_data_dataset`"
)
if args.instance_data_dir is not None:
if not os.path.exists(args.instance_data_dir):
raise ValueError(f"Does not exist: `--args.instance_data_dir` {args.instance_data_dir}")
if args.instance_data_image is not None:
if not os.path.exists(args.instance_data_image):
raise ValueError(f"Does not exist: `--args.instance_data_image` {args.instance_data_image}")
if args.instance_data_dataset is not None and (args.image_key is None or args.prompt_key is None):
raise ValueError("`--instance_data_dataset` requires setting `--image_key` and `--prompt_key`")
return args
class InstanceDataRootDataset(Dataset):
def __init__(
self,
instance_data_root,
tokenizer,
size=512,
):
self.size = size
self.tokenizer = tokenizer
self.instance_images_path = list(Path(instance_data_root).iterdir())
def __len__(self):
return len(self.instance_images_path)
def __getitem__(self, index):
image_path = self.instance_images_path[index % len(self.instance_images_path)]
instance_image = Image.open(image_path)
rv = process_image(instance_image, self.size)
prompt = os.path.splitext(os.path.basename(image_path))[0]
rv["prompt_input_ids"] = tokenize_prompt(self.tokenizer, prompt)[0]
return rv
class InstanceDataImageDataset(Dataset):
def __init__(
self,
instance_data_image,
train_batch_size,
size=512,
):
self.value = process_image(Image.open(instance_data_image), size)
self.train_batch_size = train_batch_size
def __len__(self):
# Needed so a full batch of the data can be returned. Otherwise will return
# batches of size 1
return self.train_batch_size
def __getitem__(self, index):
return self.value
class HuggingFaceDataset(Dataset):
def __init__(
self,
hf_dataset,
tokenizer,
image_key,
prompt_key,
prompt_prefix=None,
size=512,
):
self.size = size
self.image_key = image_key
self.prompt_key = prompt_key
self.tokenizer = tokenizer
self.hf_dataset = hf_dataset
self.prompt_prefix = prompt_prefix
def __len__(self):
return len(self.hf_dataset)
def __getitem__(self, index):
item = self.hf_dataset[index]
rv = process_image(item[self.image_key], self.size)
prompt = item[self.prompt_key]
if self.prompt_prefix is not None:
prompt = self.prompt_prefix + prompt
rv["prompt_input_ids"] = tokenize_prompt(self.tokenizer, prompt)[0]
return rv
def process_image(image, size):
image = exif_transpose(image)
if not image.mode == "RGB":
image = image.convert("RGB")
orig_height = image.height
orig_width = image.width
image = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)(image)
c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(size, size))
image = transforms.functional.crop(image, c_top, c_left, size, size)
image = transforms.ToTensor()(image)
micro_conds = torch.tensor(
[orig_width, orig_height, c_top, c_left, 6.0],
)
return {"image": image, "micro_conds": micro_conds}
def tokenize_prompt(tokenizer, prompt):
return tokenizer(
prompt,
truncation=True,
padding="max_length",
max_length=77,
return_tensors="pt",
).input_ids
def encode_prompt(text_encoder, input_ids):
outputs = text_encoder(input_ids, return_dict=True, output_hidden_states=True)
encoder_hidden_states = outputs.hidden_states[-2]
cond_embeds = outputs[0]
return encoder_hidden_states, cond_embeds
def main(args):
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if accelerator.is_main_process:
os.makedirs(args.output_dir, exist_ok=True)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_main_process:
accelerator.init_trackers("amused", config=vars(copy.deepcopy(args)))
if args.seed is not None:
set_seed(args.seed)
# TODO - will have to fix loading if training text encoder
text_encoder = CLIPTextModelWithProjection.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, variant=args.variant
)
vq_model = VQModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vqvae", revision=args.revision, variant=args.variant
)
if args.train_text_encoder:
if args.text_encoder_use_lora:
lora_config = LoraConfig(
r=args.text_encoder_lora_r,
lora_alpha=args.text_encoder_lora_alpha,
target_modules=args.text_encoder_lora_target_modules,
)
text_encoder.add_adapter(lora_config)
text_encoder.train()
text_encoder.requires_grad_(True)
else:
text_encoder.eval()
text_encoder.requires_grad_(False)
vq_model.requires_grad_(False)
model = UVit2DModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="transformer",
revision=args.revision,
variant=args.variant,
)
if args.use_lora:
lora_config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
target_modules=args.lora_target_modules,
)
model.add_adapter(lora_config)
model.train()
if args.gradient_checkpointing:
model.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder.gradient_checkpointing_enable()
if args.use_ema:
ema = EMAModel(
model.parameters(),
decay=args.ema_decay,
update_after_step=args.ema_update_after_step,
model_cls=UVit2DModel,
model_config=model.config,
)
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_lora_layers_to_save = None
text_encoder_lora_layers_to_save = None
for model_ in models:
if isinstance(model_, type(accelerator.unwrap_model(model))):
if args.use_lora:
transformer_lora_layers_to_save = get_peft_model_state_dict(model_)
else:
model_.save_pretrained(os.path.join(output_dir, "transformer"))
elif isinstance(model_, type(accelerator.unwrap_model(text_encoder))):
if args.text_encoder_use_lora:
text_encoder_lora_layers_to_save = get_peft_model_state_dict(model_)
else:
model_.save_pretrained(os.path.join(output_dir, "text_encoder"))
else:
raise ValueError(f"unexpected save model: {model_.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
if transformer_lora_layers_to_save is not None or text_encoder_lora_layers_to_save is not None:
AmusedLoraLoaderMixin.save_lora_weights(
output_dir,
transformer_lora_layers=transformer_lora_layers_to_save,
text_encoder_lora_layers=text_encoder_lora_layers_to_save,
)
if args.use_ema:
ema.save_pretrained(os.path.join(output_dir, "ema_model"))
def load_model_hook(models, input_dir):
transformer = None
text_encoder_ = None
while len(models) > 0:
model_ = models.pop()
if isinstance(model_, type(accelerator.unwrap_model(model))):
if args.use_lora:
transformer = model_
else:
load_model = UVit2DModel.from_pretrained(os.path.join(input_dir, "transformer"))
model_.load_state_dict(load_model.state_dict())
del load_model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder))):
if args.text_encoder_use_lora:
text_encoder_ = model_
else:
load_model = CLIPTextModelWithProjection.from_pretrained(os.path.join(input_dir, "text_encoder"))
model_.load_state_dict(load_model.state_dict())
del load_model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
if transformer is not None or text_encoder_ is not None:
lora_state_dict, network_alphas = AmusedLoraLoaderMixin.lora_state_dict(input_dir)
AmusedLoraLoaderMixin.load_lora_into_text_encoder(
lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_
)
AmusedLoraLoaderMixin.load_lora_into_transformer(
lora_state_dict, network_alphas=network_alphas, transformer=transformer
)
if args.use_ema:
load_from = EMAModel.from_pretrained(os.path.join(input_dir, "ema_model"), model_cls=UVit2DModel)
ema.load_state_dict(load_from.state_dict())
del load_from
accelerator.register_load_state_pre_hook(load_model_hook)
accelerator.register_save_state_pre_hook(save_model_hook)
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
)
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
# no decay on bias and layernorm and embedding
no_decay = ["bias", "layer_norm.weight", "mlm_ln.weight", "embeddings.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.adam_weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
if args.train_text_encoder:
optimizer_grouped_parameters.append(
{"params": text_encoder.parameters(), "weight_decay": args.adam_weight_decay}
)
optimizer = optimizer_cls(
optimizer_grouped_parameters,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
logger.info("Creating dataloaders and lr_scheduler")
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
if args.instance_data_dir is not None:
dataset = InstanceDataRootDataset(
instance_data_root=args.instance_data_dir,
tokenizer=tokenizer,
size=args.resolution,
)
elif args.instance_data_image is not None:
dataset = InstanceDataImageDataset(
instance_data_image=args.instance_data_image,
train_batch_size=args.train_batch_size,
size=args.resolution,
)
elif args.instance_data_dataset is not None:
dataset = HuggingFaceDataset(
hf_dataset=load_dataset(args.instance_data_dataset, split="train"),
tokenizer=tokenizer,
image_key=args.image_key,
prompt_key=args.prompt_key,
prompt_prefix=args.prompt_prefix,
size=args.resolution,
)
else:
assert False
train_dataloader = DataLoader(
dataset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=args.dataloader_num_workers,
collate_fn=default_collate,
)
train_dataloader.num_batches = len(train_dataloader)
lr_scheduler = diffusers.optimization.get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
)
logger.info("Preparing model, optimizer and dataloaders")
if args.train_text_encoder:
model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare(
model, optimizer, lr_scheduler, train_dataloader, text_encoder
)
else:
model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(
model, optimizer, lr_scheduler, train_dataloader
)
train_dataloader.num_batches = len(train_dataloader)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if not args.train_text_encoder:
text_encoder.to(device=accelerator.device, dtype=weight_dtype)
vq_model.to(device=accelerator.device)
if args.use_ema:
ema.to(accelerator.device)
with nullcontext() if args.train_text_encoder else torch.no_grad():
empty_embeds, empty_clip_embeds = encode_prompt(
text_encoder, tokenize_prompt(tokenizer, "").to(text_encoder.device, non_blocking=True)
)
# There is a single image, we can just pre-encode the single prompt
if args.instance_data_image is not None:
prompt = os.path.splitext(os.path.basename(args.instance_data_image))[0]
encoder_hidden_states, cond_embeds = encode_prompt(
text_encoder, tokenize_prompt(tokenizer, prompt).to(text_encoder.device, non_blocking=True)
)
encoder_hidden_states = encoder_hidden_states.repeat(args.train_batch_size, 1, 1)
cond_embeds = cond_embeds.repeat(args.train_batch_size, 1)
# 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(train_dataloader.num_batches / args.gradient_accumulation_steps)
# Afterwards we recalculate our number of training epochs.
# Note: We are not doing epoch based training here, but just using this for book keeping and being able to
# reuse the same training loop with other datasets/loaders.
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Train!
logger.info("***** Running training *****")
logger.info(f" Num training steps = {args.max_train_steps}")
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}")
resume_from_checkpoint = args.resume_from_checkpoint
if resume_from_checkpoint:
if resume_from_checkpoint == "latest":
# 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]))
if len(dirs) > 0:
resume_from_checkpoint = os.path.join(args.output_dir, dirs[-1])
else:
resume_from_checkpoint = None
if resume_from_checkpoint is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
else:
accelerator.print(f"Resuming from checkpoint {resume_from_checkpoint}")
if resume_from_checkpoint is None:
global_step = 0
first_epoch = 0
else:
accelerator.load_state(resume_from_checkpoint)
global_step = int(os.path.basename(resume_from_checkpoint).split("-")[1])
first_epoch = global_step // num_update_steps_per_epoch
# As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to
# reuse the same training loop with other datasets/loaders.
for epoch in range(first_epoch, num_train_epochs):
for batch in train_dataloader:
with torch.no_grad():
micro_conds = batch["micro_conds"].to(accelerator.device, non_blocking=True)
pixel_values = batch["image"].to(accelerator.device, non_blocking=True)
batch_size = pixel_values.shape[0]
split_batch_size = args.split_vae_encode if args.split_vae_encode is not None else batch_size
num_splits = math.ceil(batch_size / split_batch_size)
image_tokens = []
for i in range(num_splits):
start_idx = i * split_batch_size
end_idx = min((i + 1) * split_batch_size, batch_size)
bs = pixel_values.shape[0]
image_tokens.append(
vq_model.quantize(vq_model.encode(pixel_values[start_idx:end_idx]).latents)[2][2].reshape(
bs, -1
)
)
image_tokens = torch.cat(image_tokens, dim=0)
batch_size, seq_len = image_tokens.shape
timesteps = torch.rand(batch_size, device=image_tokens.device)
mask_prob = torch.cos(timesteps * math.pi * 0.5)
mask_prob = mask_prob.clip(args.min_masking_rate)
num_token_masked = (seq_len * mask_prob).round().clamp(min=1)
batch_randperm = torch.rand(batch_size, seq_len, device=image_tokens.device).argsort(dim=-1)
mask = batch_randperm < num_token_masked.unsqueeze(-1)
mask_id = accelerator.unwrap_model(model).config.vocab_size - 1
input_ids = torch.where(mask, mask_id, image_tokens)
labels = torch.where(mask, image_tokens, -100)
if args.cond_dropout_prob > 0.0:
assert encoder_hidden_states is not None
batch_size = encoder_hidden_states.shape[0]
mask = (
torch.zeros((batch_size, 1, 1), device=encoder_hidden_states.device).float().uniform_(0, 1)
< args.cond_dropout_prob
)
empty_embeds_ = empty_embeds.expand(batch_size, -1, -1)
encoder_hidden_states = torch.where(
(encoder_hidden_states * mask).bool(), encoder_hidden_states, empty_embeds_
)
empty_clip_embeds_ = empty_clip_embeds.expand(batch_size, -1)
cond_embeds = torch.where((cond_embeds * mask.squeeze(-1)).bool(), cond_embeds, empty_clip_embeds_)
bs = input_ids.shape[0]
vae_scale_factor = 2 ** (len(vq_model.config.block_out_channels) - 1)
resolution = args.resolution // vae_scale_factor
input_ids = input_ids.reshape(bs, resolution, resolution)
if "prompt_input_ids" in batch:
with nullcontext() if args.train_text_encoder else torch.no_grad():
encoder_hidden_states, cond_embeds = encode_prompt(
text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True)
)
# Train Step
with accelerator.accumulate(model):
codebook_size = accelerator.unwrap_model(model).config.codebook_size
logits = (
model(
input_ids=input_ids,
encoder_hidden_states=encoder_hidden_states,
micro_conds=micro_conds,
pooled_text_emb=cond_embeds,
)
.reshape(bs, codebook_size, -1)
.permute(0, 2, 1)
.reshape(-1, codebook_size)
)
loss = F.cross_entropy(
logits,
labels.view(-1),
ignore_index=-100,
reduction="mean",
)
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
avg_masking_rate = accelerator.gather(mask_prob.repeat(args.train_batch_size)).mean()
accelerator.backward(loss)
if args.max_grad_norm is not None and accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema.step(model.parameters())
if (global_step + 1) % args.logging_steps == 0:
logs = {
"step_loss": avg_loss.item(),
"lr": lr_scheduler.get_last_lr()[0],
"avg_masking_rate": avg_masking_rate.item(),
}
accelerator.log(logs, step=global_step + 1)
logger.info(
f"Step: {global_step + 1} "
f"Loss: {avg_loss.item():0.4f} "
f"LR: {lr_scheduler.get_last_lr()[0]:0.6f}"
)
if (global_step + 1) % args.checkpointing_steps == 0:
save_checkpoint(args, accelerator, global_step + 1)
if (global_step + 1) % args.validation_steps == 0 and accelerator.is_main_process:
if args.use_ema:
ema.store(model.parameters())
ema.copy_to(model.parameters())
with torch.no_grad():
logger.info("Generating images...")
model.eval()
if args.train_text_encoder:
text_encoder.eval()
scheduler = AmusedScheduler.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="scheduler",
revision=args.revision,
variant=args.variant,
)
pipe = AmusedPipeline(
transformer=accelerator.unwrap_model(model),
tokenizer=tokenizer,
text_encoder=text_encoder,
vqvae=vq_model,
scheduler=scheduler,
)
pil_images = pipe(prompt=args.validation_prompts).images
wandb_images = [
wandb.Image(image, caption=args.validation_prompts[i])
for i, image in enumerate(pil_images)
]
wandb.log({"generated_images": wandb_images}, step=global_step + 1)
model.train()
if args.train_text_encoder:
text_encoder.train()
if args.use_ema:
ema.restore(model.parameters())
global_step += 1
# Stop training if max steps is reached
if global_step >= args.max_train_steps:
break
# End for
accelerator.wait_for_everyone()
# Evaluate and save checkpoint at the end of training
save_checkpoint(args, accelerator, global_step)
# Save the final trained checkpoint
if accelerator.is_main_process:
model = accelerator.unwrap_model(model)
if args.use_ema:
ema.copy_to(model.parameters())
model.save_pretrained(args.output_dir)
accelerator.end_training()
def save_checkpoint(args, accelerator, global_step):
output_dir = args.output_dir
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if accelerator.is_main_process and args.checkpoints_total_limit is not None:
checkpoints = os.listdir(output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = Path(output_dir) / f"checkpoint-{global_step}"
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if __name__ == "__main__":
main(parse_args())