flowar-i2sb-checkpoints

FlowAR + I2SB hybrid - training checkpoints

Checkpoints from the flowar-variant branch of forward-free-duffiser: a scale-wise autoregressive generator (FlowAR) whose FINE scales are produced by an I2SB Schrodinger-bridge refiner that deblurs the regridded previous scale, while the COARSE scales keep FlowAR flow matching. Class-conditional ImageNet-256, KL-16 VAE latents, scales 1/2/4/8/16.

These are mid-training checkpoints, not final results.

Training recipe (identical across all three runs)

Effective batch 2048 (per-GPU batch x grad-accum x 4 GPUs), base lr 5e-5 scaled to a peak of 4e-4, 100-epoch warmup then cosine decay over 400 epochs, bf16 autocast, weight decay 0.02 with the generator heads exempt, EMA 0.9999.

Checkpoint contents

A checkpoint-epN.pth is a torch.save dict with model, model_ema, optimizer, scaler, epoch and the training args. An ema-epN.pt carries only model_ema, epoch and args, which is everything needed to sample or evaluate. Always evaluate with the EMA weights. Load with torch.load(..., weights_only=False), because args is a pickled Namespace.

Sampling notes

The fine-scale I2SB bridge keeps improving with more sampling steps -- use >=100 (--num_step 100); flow matching is flat at ~25 steps. Classifier-free guidance matters a great deal here: evaluate at cfg 2.4-3.0, never at 1.0.