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Train Super-Resolution model (Stage 2: 1K→2K, Stage 3: 2K→4K).
Uses a diffusion-based SR approach similar to StableSR/SUPIR.
"""
import argparse
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
from accelerate import Accelerator
from accelerate.utils import ProjectConfiguration
from tqdm import tqdm
import wandb
class SRDataset(Dataset):
def __init__(self, input_dir, target_dir, input_size, target_size):
self.input_dir = Path(input_dir)
self.target_dir = Path(target_dir)
self.input_size = input_size
self.target_size = target_size
self.input_files = sorted(self.input_dir.glob("*.png"))
self.target_files = sorted(self.target_dir.glob("*.png"))
# Match by filename
input_stems = {f.stem: f for f in self.input_files}
target_stems = {f.stem: f for f in self.target_files}
common = set(input_stems.keys()) & set(target_stems.keys())
self.pairs = [(input_stems[s], target_stems[s]) for s in sorted(common)]
print(f"Found {len(self.pairs)} image pairs")
self.input_transform = transforms.Compose([
transforms.Resize((input_size, input_size), interpolation=transforms.InterpolationMode.LANCZOS),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
self.target_transform = transforms.Compose([
transforms.Resize((target_size, target_size), interpolation=transforms.InterpolationMode.LANCZOS),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
def __len__(self):
return len(self.pairs)
def __getitem__(self, idx):
input_path, target_path = self.pairs[idx]
input_img = Image.open(input_path).convert("RGB")
target_img = Image.open(target_path).convert("RGB")
return {
"input": self.input_transform(input_img),
"target": self.target_transform(target_img),
}
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
self.norm1 = nn.GroupNorm(8, channels)
self.norm2 = nn.GroupNorm(8, channels)
def forward(self, x):
residual = x
x = F.silu(self.norm1(self.conv1(x)))
x = self.norm2(self.conv2(x))
return x + residual
class SRUNet(nn.Module):
"""Simple U-Net based super-resolution model."""
def __init__(self, in_channels=3, out_channels=3, base_channels=64, scale_factor=2):
super().__init__()
self.scale_factor = scale_factor
# Encoder
self.enc1 = nn.Sequential(
nn.Conv2d(in_channels, base_channels, 3, padding=1),
ResidualBlock(base_channels),
ResidualBlock(base_channels),
)
self.enc2 = nn.Sequential(
nn.Conv2d(base_channels, base_channels * 2, 3, stride=2, padding=1),
ResidualBlock(base_channels * 2),
ResidualBlock(base_channels * 2),
)
self.enc3 = nn.Sequential(
nn.Conv2d(base_channels * 2, base_channels * 4, 3, stride=2, padding=1),
ResidualBlock(base_channels * 4),
ResidualBlock(base_channels * 4),
)
# Bottleneck
self.bottleneck = nn.Sequential(
ResidualBlock(base_channels * 4),
ResidualBlock(base_channels * 4),
)
# Decoder
self.dec3 = nn.Sequential(
nn.ConvTranspose2d(base_channels * 4, base_channels * 2, 4, stride=2, padding=1),
ResidualBlock(base_channels * 2),
ResidualBlock(base_channels * 2),
)
self.dec2 = nn.Sequential(
nn.ConvTranspose2d(base_channels * 4, base_channels, 4, stride=2, padding=1),
ResidualBlock(base_channels),
ResidualBlock(base_channels),
)
self.dec1 = nn.Sequential(
ResidualBlock(base_channels * 2),
ResidualBlock(base_channels),
)
# Upscale + output
self.upscale = nn.Sequential(
nn.Conv2d(base_channels, base_channels * (scale_factor ** 2), 3, padding=1),
nn.PixelShuffle(scale_factor),
nn.Conv2d(base_channels, out_channels, 3, padding=1),
nn.Tanh(),
)
def forward(self, x):
# Encoder
e1 = self.enc1(x)
e2 = self.enc2(e1)
e3 = self.enc3(e2)
# Bottleneck
b = self.bottleneck(e3)
# Decoder with skip connections
d3 = self.dec3(b)
d3 = torch.cat([d3, e2], dim=1)
d2 = self.dec2(d3)
d2 = torch.cat([d2, e1], dim=1)
d1 = self.dec1(d2)
# Upscale
out = self.upscale(d1)
return out
class PerceptualLoss(nn.Module):
"""VGG-based perceptual loss."""
def __init__(self):
super().__init__()
from torchvision.models import vgg19, VGG19_Weights
vgg = vgg19(weights=VGG19_Weights.DEFAULT).features
self.blocks = nn.ModuleList([
vgg[:4], # relu1_2
vgg[4:9], # relu2_2
vgg[9:18], # relu3_4
])
for p in self.parameters():
p.requires_grad = False
def forward(self, x, target):
loss = 0.0
for block in self.blocks:
x = block(x)
with torch.no_grad():
target = block(target)
loss += F.l1_loss(x, target)
return loss
def main():
parser = argparse.ArgumentParser(description="Train SR model")
parser.add_argument("--stage", type=int, choices=[2, 3], required=True, help="Stage 2 (1K→2K) or Stage 3 (2K→4K)")
parser.add_argument("--input-dir", type=Path, default=None)
parser.add_argument("--target-dir", type=Path, default=None)
parser.add_argument("--output-dir", type=Path, default=None)
parser.add_argument("--batch-size", type=int, default=4)
parser.add_argument("--learning-rate", type=float, default=2e-4)
parser.add_argument("--max-steps", type=int, default=200000)
parser.add_argument("--save-steps", type=int, default=10000)
parser.add_argument("--base-channels", type=int, default=64)
parser.add_argument("--perceptual-weight", type=float, default=0.1)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--use-wandb", action="store_true")
args = parser.parse_args()
# Set defaults based on stage
sr_pairs_dir = Path("/home/adminuser/chungcat/data/processed/sr_pairs")
if args.stage == 2:
args.input_dir = args.input_dir or sr_pairs_dir / "1k_input"
args.target_dir = args.target_dir or sr_pairs_dir / "2k_target"
args.output_dir = args.output_dir or Path("/home/adminuser/chungcat/checkpoints/sr_stage2")
input_size, target_size = 1024, 2048
else:
args.input_dir = args.input_dir or sr_pairs_dir / "2k_input"
args.target_dir = args.target_dir or sr_pairs_dir / "4k_target"
args.output_dir = args.output_dir or Path("/home/adminuser/chungcat/checkpoints/sr_stage3")
input_size, target_size = 2048, 4096
# Accelerator
project_config = ProjectConfiguration(project_dir=str(args.output_dir))
accelerator = Accelerator(
mixed_precision="bf16",
project_config=project_config,
)
if accelerator.is_main_process:
args.output_dir.mkdir(parents=True, exist_ok=True)
if args.use_wandb:
wandb.init(project=f"sr-stage{args.stage}", config=vars(args))
# Model
model = SRUNet(
in_channels=3,
out_channels=3,
base_channels=args.base_channels,
scale_factor=2,
)
# Losses
l1_loss = nn.L1Loss()
perceptual_loss = PerceptualLoss()
# Optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.max_steps)
# Dataset
dataset = SRDataset(args.input_dir, args.target_dir, input_size, target_size)
dataloader = DataLoader(
dataset, batch_size=args.batch_size, shuffle=True,
num_workers=8, pin_memory=True, drop_last=True,
)
# Prepare
model, optimizer, dataloader, scheduler = accelerator.prepare(
model, optimizer, dataloader, scheduler
)
perceptual_loss.to(accelerator.device)
# Training
global_step = 0
print(f"Starting SR Stage {args.stage} training...")
print(f" Input: {input_size}px → Target: {target_size}px")
print(f" Dataset: {len(dataset)} pairs")
print(f" Max steps: {args.max_steps}")
model.train()
while global_step < args.max_steps:
for batch in dataloader:
if global_step >= args.max_steps:
break
input_imgs = batch["input"]
target_imgs = batch["target"]
# Forward
pred = model(input_imgs)
# Resize pred to match target if needed
if pred.shape != target_imgs.shape:
pred = F.interpolate(pred, size=target_imgs.shape[2:], mode="bilinear", align_corners=False)
# Losses
loss_l1 = l1_loss(pred, target_imgs)
loss_perceptual = perceptual_loss(pred, target_imgs)
loss = loss_l1 + args.perceptual_weight * loss_perceptual
# Backward
accelerator.backward(loss)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
if global_step % 100 == 0 and accelerator.is_main_process:
print(f"Step {global_step}/{args.max_steps} | L1: {loss_l1.item():.4f} | Perceptual: {loss_perceptual.item():.4f} | Total: {loss.item():.4f}")
if args.use_wandb:
wandb.log({
"loss_l1": loss_l1.item(),
"loss_perceptual": loss_perceptual.item(),
"loss_total": loss.item(),
"lr": scheduler.get_last_lr()[0],
"step": global_step,
})
if global_step % args.save_steps == 0 and accelerator.is_main_process:
save_path = args.output_dir / f"checkpoint-{global_step}"
save_path.mkdir(parents=True, exist_ok=True)
torch.save(
accelerator.unwrap_model(model).state_dict(),
save_path / "model.pt",
)
print(f"Saved checkpoint: {save_path}")
# Save final
if accelerator.is_main_process:
final_path = args.output_dir / "final"
final_path.mkdir(parents=True, exist_ok=True)
torch.save(
accelerator.unwrap_model(model).state_dict(),
final_path / "model.pt",
)
print(f"Training complete! Model saved to {final_path}")
if args.use_wandb:
wandb.finish()
if __name__ == "__main__":
main()
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