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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# 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 logging
import math
import os
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 torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from tqdm.auto import tqdm
from diffusers import AutoencoderRAE
from diffusers.optimization import get_scheduler
logger = get_logger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Train a stage-1 Representation Autoencoder (RAE) decoder.")
parser.add_argument(
"--train_data_dir",
type=str,
required=True,
help="Path to an ImageFolder-style dataset root.",
)
parser.add_argument(
"--output_dir", type=str, default="autoencoder-rae", help="Directory to save checkpoints/model."
)
parser.add_argument("--logging_dir", type=str, default="logs", help="Accelerate logging directory.")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--resolution", type=int, default=256)
parser.add_argument("--center_crop", action="store_true")
parser.add_argument("--random_flip", action="store_true")
parser.add_argument("--train_batch_size", type=int, default=8)
parser.add_argument("--dataloader_num_workers", type=int, default=4)
parser.add_argument("--num_train_epochs", type=int, default=10)
parser.add_argument("--max_train_steps", type=int, default=None)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--max_grad_norm", type=float, default=1.0)
parser.add_argument("--learning_rate", type=float, default=1e-4)
parser.add_argument("--adam_beta1", type=float, default=0.9)
parser.add_argument("--adam_beta2", type=float, default=0.999)
parser.add_argument("--adam_weight_decay", type=float, default=1e-2)
parser.add_argument("--adam_epsilon", type=float, default=1e-8)
parser.add_argument("--lr_scheduler", type=str, default="cosine")
parser.add_argument("--lr_warmup_steps", type=int, default=500)
parser.add_argument("--checkpointing_steps", type=int, default=1000)
parser.add_argument("--validation_steps", type=int, default=500)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
help="Path to a pretrained AutoencoderRAE model (or HF Hub id) to resume training from.",
)
parser.add_argument(
"--encoder_name_or_path",
type=str,
default=None,
help=(
"HF Hub id or local path of the pretrained encoder (e.g. 'facebook/dinov2-with-registers-base'). "
"When --pretrained_model_name_or_path is not set, the encoder weights are loaded from this path "
"into a freshly constructed AutoencoderRAE. Ignored when --pretrained_model_name_or_path is set."
),
)
parser.add_argument("--encoder_type", type=str, choices=["dinov2", "siglip2", "mae"], default="dinov2")
parser.add_argument("--encoder_hidden_size", type=int, default=768)
parser.add_argument("--encoder_patch_size", type=int, default=14)
parser.add_argument("--encoder_num_hidden_layers", type=int, default=12)
parser.add_argument("--encoder_input_size", type=int, default=224)
parser.add_argument("--patch_size", type=int, default=16)
parser.add_argument("--image_size", type=int, default=256)
parser.add_argument("--num_channels", type=int, default=3)
parser.add_argument("--decoder_hidden_size", type=int, default=1152)
parser.add_argument("--decoder_num_hidden_layers", type=int, default=28)
parser.add_argument("--decoder_num_attention_heads", type=int, default=16)
parser.add_argument("--decoder_intermediate_size", type=int, default=4096)
parser.add_argument("--noise_tau", type=float, default=0.0)
parser.add_argument("--scaling_factor", type=float, default=1.0)
parser.add_argument("--reshape_to_2d", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument(
"--reconstruction_loss_type",
type=str,
choices=["l1", "mse"],
default="l1",
help="Pixel reconstruction loss.",
)
parser.add_argument(
"--encoder_loss_weight",
type=float,
default=0.0,
help="Weight for encoder feature consistency loss in the training loop.",
)
parser.add_argument(
"--use_encoder_loss",
action="store_true",
help="Enable encoder feature consistency loss term in the training loop.",
)
parser.add_argument("--report_to", type=str, default="tensorboard")
return parser.parse_args()
def build_transforms(args):
image_transforms = [
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BICUBIC),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
]
if args.random_flip:
image_transforms.append(transforms.RandomHorizontalFlip())
image_transforms.append(transforms.ToTensor())
return transforms.Compose(image_transforms)
def compute_losses(
model, pixel_values, reconstruction_loss_type: str, use_encoder_loss: bool, encoder_loss_weight: float
):
decoded = model(pixel_values).sample
if decoded.shape[-2:] != pixel_values.shape[-2:]:
raise ValueError(
"Training requires matching reconstruction and target sizes, got "
f"decoded={tuple(decoded.shape[-2:])}, target={tuple(pixel_values.shape[-2:])}."
)
if reconstruction_loss_type == "l1":
reconstruction_loss = F.l1_loss(decoded.float(), pixel_values.float())
else:
reconstruction_loss = F.mse_loss(decoded.float(), pixel_values.float())
encoder_loss = torch.zeros_like(reconstruction_loss)
if use_encoder_loss and encoder_loss_weight > 0:
base_model = model.module if hasattr(model, "module") else model
target_encoder_input = base_model._resize_and_normalize(pixel_values)
reconstructed_encoder_input = base_model._resize_and_normalize(decoded)
encoder_forward_kwargs = {"model": base_model.encoder}
if base_model.config.encoder_type == "mae":
encoder_forward_kwargs["patch_size"] = base_model.config.encoder_patch_size
with torch.no_grad():
target_tokens = base_model._encoder_forward_fn(images=target_encoder_input, **encoder_forward_kwargs)
reconstructed_tokens = base_model._encoder_forward_fn(
images=reconstructed_encoder_input, **encoder_forward_kwargs
)
encoder_loss = F.mse_loss(reconstructed_tokens.float(), target_tokens.float())
loss = reconstruction_loss + float(encoder_loss_weight) * encoder_loss
return decoded, loss, reconstruction_loss, encoder_loss
def _strip_final_layernorm_affine(state_dict, prefix=""):
"""Remove final layernorm weight/bias so the model keeps its default init (identity)."""
keys_to_strip = {f"{prefix}weight", f"{prefix}bias"}
return {k: v for k, v in state_dict.items() if k not in keys_to_strip}
def _load_pretrained_encoder_weights(model, encoder_type, encoder_name_or_path):
"""Load pretrained HF transformers encoder weights into the model's encoder."""
if encoder_type == "dinov2":
from transformers import Dinov2WithRegistersModel
hf_encoder = Dinov2WithRegistersModel.from_pretrained(encoder_name_or_path)
state_dict = hf_encoder.state_dict()
state_dict = _strip_final_layernorm_affine(state_dict, prefix="layernorm.")
elif encoder_type == "siglip2":
from transformers import SiglipModel
hf_encoder = SiglipModel.from_pretrained(encoder_name_or_path).vision_model
state_dict = {f"vision_model.{k}": v for k, v in hf_encoder.state_dict().items()}
state_dict = _strip_final_layernorm_affine(state_dict, prefix="vision_model.post_layernorm.")
elif encoder_type == "mae":
from transformers import ViTMAEForPreTraining
hf_encoder = ViTMAEForPreTraining.from_pretrained(encoder_name_or_path).vit
state_dict = hf_encoder.state_dict()
state_dict = _strip_final_layernorm_affine(state_dict, prefix="layernorm.")
else:
raise ValueError(f"Unknown encoder_type: {encoder_type}")
model.encoder.load_state_dict(state_dict, strict=False)
def main():
args = parse_args()
if args.resolution != args.image_size:
raise ValueError(
f"`--resolution` ({args.resolution}) must match `--image_size` ({args.image_size}) "
"for stage-1 reconstruction loss."
)
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,
project_config=accelerator_project_config,
log_with=args.report_to,
)
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 args.seed is not None:
set_seed(args.seed)
if accelerator.is_main_process:
os.makedirs(args.output_dir, exist_ok=True)
accelerator.wait_for_everyone()
dataset = ImageFolder(args.train_data_dir, transform=build_transforms(args))
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples]).float()
return {"pixel_values": pixel_values}
train_dataloader = DataLoader(
dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
pin_memory=True,
drop_last=True,
)
if args.pretrained_model_name_or_path is not None:
model = AutoencoderRAE.from_pretrained(args.pretrained_model_name_or_path)
logger.info(f"Loaded pretrained AutoencoderRAE from {args.pretrained_model_name_or_path}")
else:
model = AutoencoderRAE(
encoder_type=args.encoder_type,
encoder_hidden_size=args.encoder_hidden_size,
encoder_patch_size=args.encoder_patch_size,
encoder_num_hidden_layers=args.encoder_num_hidden_layers,
decoder_hidden_size=args.decoder_hidden_size,
decoder_num_hidden_layers=args.decoder_num_hidden_layers,
decoder_num_attention_heads=args.decoder_num_attention_heads,
decoder_intermediate_size=args.decoder_intermediate_size,
patch_size=args.patch_size,
encoder_input_size=args.encoder_input_size,
image_size=args.image_size,
num_channels=args.num_channels,
noise_tau=args.noise_tau,
reshape_to_2d=args.reshape_to_2d,
use_encoder_loss=args.use_encoder_loss,
scaling_factor=args.scaling_factor,
)
if args.encoder_name_or_path is not None:
_load_pretrained_encoder_weights(model, args.encoder_type, args.encoder_name_or_path)
logger.info(f"Loaded pretrained encoder weights from {args.encoder_name_or_path}")
model.encoder.requires_grad_(False)
model.decoder.requires_grad_(True)
model.train()
optimizer = torch.optim.AdamW(
(p for p in model.parameters() if p.requires_grad),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
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 * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
if overrode_max_train_steps:
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
if accelerator.is_main_process:
accelerator.init_trackers("train_autoencoder_rae", config=vars(args))
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(dataset)}")
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" Total optimization steps = {args.max_train_steps}")
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):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(model):
pixel_values = batch["pixel_values"]
_, loss, reconstruction_loss, encoder_loss = compute_losses(
model,
pixel_values,
reconstruction_loss_type=args.reconstruction_loss_type,
use_encoder_loss=args.use_encoder_loss,
encoder_loss_weight=args.encoder_loss_weight,
)
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
logs = {
"loss": loss.detach().item(),
"reconstruction_loss": reconstruction_loss.detach().item(),
"encoder_loss": encoder_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.validation_steps == 0:
with torch.no_grad():
_, val_loss, val_reconstruction_loss, val_encoder_loss = compute_losses(
model,
pixel_values,
reconstruction_loss_type=args.reconstruction_loss_type,
use_encoder_loss=args.use_encoder_loss,
encoder_loss_weight=args.encoder_loss_weight,
)
accelerator.log(
{
"val/loss": val_loss.detach().item(),
"val/reconstruction_loss": val_reconstruction_loss.detach().item(),
"val/encoder_loss": val_encoder_loss.detach().item(),
},
step=global_step,
)
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(save_path)
logger.info(f"Saved checkpoint to {save_path}")
if global_step >= args.max_train_steps:
break
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.output_dir)
accelerator.end_training()
if __name__ == "__main__":
main()
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