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IRIS Training Script
=====================
End-to-end training pipeline for IRIS (Iterative Recurrent Image Synthesis).
Supports:
- Stage 1: Wavelet VAE pre-training (reconstruction)
- Stage 2: Class-conditional pretraining (ImageNet)
- Stage 3: Text-image alignment (CLIP-conditioned)
- Stage 4: Aesthetic fine-tuning
Usage:
python train_iris.py --stage 1 --dataset imagenet --epochs 50
python train_iris.py --stage 3 --dataset cc3m --epochs 100
Designed to run on Colab/Kaggle (single GPU, T4/A100).
"""
import os
import math
import argparse
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torch.cuda.amp import autocast, GradScaler
from pathlib import Path
from iris_model import (
IRIS, IRISConfig, WaveletVAE,
create_iris_small, create_iris_tiny, create_iris_base,
count_parameters, estimate_memory_mb,
)
# ============================================================================
# Synthetic Dataset (for testing; replace with real dataset loaders)
# ============================================================================
class SyntheticImageTextDataset(Dataset):
"""Synthetic dataset for testing the training pipeline."""
def __init__(self, num_samples=1000, image_size=256, text_dim=768, text_len=77):
self.num_samples = num_samples
self.image_size = image_size
self.text_dim = text_dim
self.text_len = text_len
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
image = torch.randn(3, self.image_size, self.image_size)
text = torch.randn(self.text_len, self.text_dim)
return image, text
# ============================================================================
# VAE Training (Stage 1)
# ============================================================================
def train_vae(config: IRISConfig, args):
"""Train the Wavelet VAE for image reconstruction."""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Training VAE on {device}")
vae = WaveletVAE(config).to(device)
print(f"VAE params: {sum(p.numel() for p in vae.parameters()):,}")
optimizer = torch.optim.AdamW(vae.parameters(), lr=1e-4, weight_decay=0.05)
scaler = GradScaler() if args.fp16 else None
# Input size depends on VAE architecture: DWT(2×) + down_blocks
num_downsamples = len(config.vae_channels) - 1
total_downsample = 2 * (2 ** num_downsamples) # DWT + conv downsamples
input_size = config.latent_spatial * total_downsample
dataset = SyntheticImageTextDataset(
num_samples=args.num_samples,
image_size=input_size,
)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
num_workers=2, pin_memory=True)
print(f"Input image size: {input_size}×{input_size}")
print(f"Latent size: {config.latent_spatial}×{config.latent_spatial}×{config.latent_channels}")
vae.train()
for epoch in range(args.epochs):
total_loss = 0
t0 = time.time()
for batch_idx, (images, _) in enumerate(loader):
images = images.to(device)
with autocast(enabled=args.fp16, dtype=torch.float16):
x_recon, mean, logvar = vae(images)
# Reconstruction loss (MSE + Perceptual-like via gradient)
recon_loss = F.mse_loss(x_recon, images)
# KL divergence
kl_loss = -0.5 * (1 + logvar - mean.pow(2) - logvar.exp()).mean()
# Wavelet frequency loss (enforce high-freq detail preservation)
from iris_model import HaarDWT2D
dwt = HaarDWT2D()
recon_wavelet = dwt(x_recon)
target_wavelet = dwt(images)
freq_loss = F.l1_loss(recon_wavelet, target_wavelet)
loss = recon_loss + 0.001 * kl_loss + 0.1 * freq_loss
optimizer.zero_grad()
if scaler:
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(vae.parameters(), 1.0)
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(vae.parameters(), 1.0)
optimizer.step()
total_loss += loss.item()
if batch_idx % 10 == 0:
print(f" Step {batch_idx}: loss={loss.item():.4f} "
f"(recon={recon_loss.item():.4f}, kl={kl_loss.item():.4f}, "
f"freq={freq_loss.item():.4f})")
avg_loss = total_loss / len(loader)
dt = time.time() - t0
print(f"Epoch {epoch+1}/{args.epochs}: avg_loss={avg_loss:.4f}, time={dt:.1f}s")
# Save
save_path = Path(args.output_dir) / "vae_checkpoint.pt"
save_path.parent.mkdir(parents=True, exist_ok=True)
torch.save(vae.state_dict(), save_path)
print(f"VAE saved to {save_path}")
return vae
# ============================================================================
# Generator Training (Stages 2-4)
# ============================================================================
def train_generator(config: IRISConfig, args, vae_path=None):
"""Train the IRIS generator with rectified flow."""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Training Generator on {device}")
model = IRIS(config).to(device)
# Load pretrained VAE if available
if vae_path and os.path.exists(vae_path):
model.vae.load_state_dict(torch.load(vae_path, map_location=device))
print(f"Loaded VAE from {vae_path}")
# Freeze VAE during generator training
for p in model.vae.parameters():
p.requires_grad = False
counts = count_parameters(model.generator)
print(f"Generator params: {counts['total']:,}")
print(f"Generator memory: {estimate_memory_mb(model.generator):.1f} MB (fp32)")
# Optimizer (AdamW with cosine schedule)
optimizer = torch.optim.AdamW(
model.generator.parameters(),
lr=args.lr,
weight_decay=0.03,
betas=(0.9, 0.95),
)
# Cosine LR schedule with warmup
total_steps = args.epochs * (args.num_samples // args.batch_size)
warmup_steps = min(5000, total_steps // 10)
def lr_lambda(step):
if step < warmup_steps:
return step / warmup_steps
progress = (step - warmup_steps) / (total_steps - warmup_steps)
return 0.5 * (1 + math.cos(math.pi * progress))
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
scaler = GradScaler() if args.fp16 else None
# Dataset
num_downsamples = len(config.vae_channels) - 1
total_downsample = 2 * (2 ** num_downsamples)
input_size = config.latent_spatial * total_downsample
dataset = SyntheticImageTextDataset(
num_samples=args.num_samples,
image_size=input_size,
text_dim=config.text_dim,
)
loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
num_workers=2, pin_memory=True)
print(f"Input size: {input_size}×{input_size}")
print(f"Training for {args.epochs} epochs ({total_steps} steps)")
print(f"Warmup: {warmup_steps} steps")
# Training loop
global_step = 0
model.train()
model.vae.eval()
for epoch in range(args.epochs):
epoch_loss = 0
t0 = time.time()
for batch_idx, (images, text_tokens) in enumerate(loader):
images = images.to(device)
text_tokens = text_tokens.to(device)
with autocast(enabled=args.fp16, dtype=torch.float16):
result = model.train_step(images, text_tokens)
loss = result['loss']
optimizer.zero_grad()
if scaler:
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.generator.parameters(), 1.0)
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.generator.parameters(), 1.0)
optimizer.step()
scheduler.step()
global_step += 1
epoch_loss += loss.item()
if global_step % args.log_every == 0:
lr = optimizer.param_groups[0]['lr']
print(f" Step {global_step}: loss={loss.item():.4f} "
f"(vel={result['velocity_loss']:.4f}, kl={result['kl_loss']:.4f}) "
f"lr={lr:.2e}")
avg_loss = epoch_loss / len(loader)
dt = time.time() - t0
print(f"Epoch {epoch+1}/{args.epochs}: avg_loss={avg_loss:.4f}, time={dt:.1f}s")
# Save checkpoint
if (epoch + 1) % args.save_every == 0:
save_path = Path(args.output_dir) / f"iris_epoch{epoch+1}.pt"
save_path.parent.mkdir(parents=True, exist_ok=True)
torch.save({
'epoch': epoch + 1,
'global_step': global_step,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'config': config,
}, save_path)
print(f"Checkpoint saved to {save_path}")
# Final save
save_path = Path(args.output_dir) / "iris_final.pt"
torch.save({
'model_state_dict': model.state_dict(),
'config': config,
}, save_path)
print(f"Final model saved to {save_path}")
# ============================================================================
# Main
# ============================================================================
def main():
parser = argparse.ArgumentParser(description="IRIS Training Pipeline")
parser.add_argument('--stage', type=int, default=1, choices=[1, 2, 3, 4],
help='Training stage: 1=VAE, 2=class-cond, 3=text-image, 4=aesthetic')
parser.add_argument('--model-size', type=str, default='tiny', choices=['tiny', 'small', 'base'],
help='Model size variant')
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=8)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--fp16', action='store_true', default=True)
parser.add_argument('--num-samples', type=int, default=1000,
help='Number of training samples (for synthetic data)')
parser.add_argument('--output-dir', type=str, default='./checkpoints')
parser.add_argument('--vae-path', type=str, default=None,
help='Path to pretrained VAE checkpoint')
parser.add_argument('--log-every', type=int, default=10)
parser.add_argument('--save-every', type=int, default=5)
args = parser.parse_args()
# Create config based on model size
if args.model_size == 'tiny':
model = create_iris_tiny()
elif args.model_size == 'small':
model = create_iris_small()
else:
model = create_iris_base()
config = model.config
print(f"{'='*60}")
print(f"IRIS Training — Stage {args.stage} — {args.model_size}")
print(f"{'='*60}")
if args.stage == 1:
train_vae(config, args)
else:
train_generator(config, args, vae_path=args.vae_path)
if __name__ == "__main__":
main()
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