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8b0b874 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | #!/usr/bin/env bash
set -euo pipefail
cd /home/nvidia/SiT-Complementary
export WANDB_KEY
WANDB_KEY="$(python -c 'import netrc; print(netrc.netrc().authenticators("api.wandb.ai")[2])')"
# Match the base run's low-overhead W&B recording setup. This is a new run
# because its name (and therefore deterministic W&B run ID) is unique.
export WANDB_MODE=offline
export WANDB_DIR=/home/nvidia/SiT-rot-layer-bs256/wandb
mkdir -p "$WANDB_DIR"
exec torchrun \
--nnodes=1 \
--nproc_per_node=8 \
train_rot_layer.py \
--model SiT-S/2 \
--epochs 800 \
--data-path /home/nvidia/datasets/imagenet-1k/train \
--results-dir /home/nvidia/SiT-rot-layer-bs256/results-200ep \
--global-batch-size 256 \
--learning-rate 0.0001 \
--global-seed 0 \
--vae ema \
--num-workers 4 \
--log-every 100 \
--ckpt-every 50000 \
--sample-every 10000 \
--cfg-scale 4.0 \
--run-name SiT-S-2-RotLayer-bs256-lr1e-4-200ep \
--ckpt /home/nvidia/SiT-rot-layer-bs256/results-200ep/SiT-S-2-RotLayer-bs256-lr1e-4-200ep/checkpoints/1000800.pt \
--fid-every-checkpoint \
--fid-every 250000 \
--fid-num-samples 50000 \
--fid-reference /home/nvidia/evaluation/reference/discon-download/VIRTUAL_imagenet256_labeled.npz \
--fid-history /home/nvidia/SiT-rot-layer-bs256/results-200ep/SiT-S-2-RotLayer-bs256-lr1e-4-200ep/fid_cfg1_50k.tsv \
--fid-per-proc-batch-size 64 \
--fid-inception-batch-size 128 \
--fid-num-workers 8 \
--fid-sampling-steps 250 \
--fid-seed 0 \
--fid-stop-consecutive-increases 3 \
--fid-stop-min-absolute-rise 0.25 \
--fid-stop-min-relative-rise 0.005 \
--wandb
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