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run_baseline.sh
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151 lines (145 loc) · 4.03 KB
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# run shortcut diffusion with classifier-free guidance (SCD-cfg)
accelerate launch --multi_gpu train.py \
--exp-name "shortcut-b2-cfg" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--checkpointing-step 40000 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240 \
--path-type "linear" \
--loss-type "l2" \
--time-sampler "uniform" \
--ratio-r-not-equal-t 0.25 \
--cfg-omega 1.5 \
--no-debug \
--tgt-decay 0.0 \
--ema-decay 0.9999 \
--discrete-time-steps 128
# run shortcut diffusion with class condition (SCD-cnd)
accelerate launch --multi_gpu train.py \
--exp-name "shortcut-b2-cnd" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--checkpointing-step 40000 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240 \
--path-type "linear" \
--loss-type "l2" \
--time-sampler "uniform" \
--ratio-r-not-equal-t 0.25 \
--cfg-omega 1.0 \
--no-debug \
--tgt-decay 0.0 \
--ema-decay 0.9999 \
--discrete-time-steps 128
# run meanflow with classifier-free guidance (MF-cfg)
accelerate launch --multi_gpu train.py \
--exp-name "meanflow-b2-cfg" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240\
--path-type "linear" \
--loss-type "adaptive" \
--time-sampler "logit_normal" \
--time-mu -0.4 \
--time-sigma 1.0 \
--ratio-r-not-equal-t 0.25 \
--adaptive-p 1.0 \
--cfg-omega 1.0 \
--cfg-kappa 0.5 \
--cfg-min-t 0.0 \
--cfg-max-t 1.0 \
--no-debug
# run meanflow with class condition (MF-cnd) by setting cfg-min-t larger than cfg-max-t
accelerate launch --multi_gpu train.py \
--exp-name "meanflow-b2-cnd" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240 \
--path-type "linear" \
--loss-type "adaptive" \
--time-sampler "logit_normal" \
--time-mu -0.4 \
--time-sigma 1.0 \
--ratio-r-not-equal-t 0.25 \
--adaptive-p 1.0 \
--cfg-omega 1.0 \
--cfg-kappa 0.5 \
--cfg-min-t 1.0 \
--cfg-max-t 0.0 \
--no-debug
# run scm-linear with classifier-free guidance (SCM-linear-cfg)
accelerate launch --multi_gpu train.py \
--exp-name "scmlinear-b2-cfg" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240 \
--path-type "linear" \
--loss-type "l2" \
--time-sampler "logit_normal" \
--time-mu -1.0 \
--time-sigma 1.4 \
--ratio-r-not-equal-t 0.25 \
--cfg-omega 1.0 \
--cfg-kappa 0.5 \
--cfg-min-t 1.0 \
--cfg-max-t 0.0 \
--variational-adaptive-weight \
--no-debug
# run scm-cosine with class condition (SCM-cosine-cnd)
accelerate launch --multi_gpu train.py \
--exp-name "scmcosine-b2-cnd" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 256 \
--allow-tf32 \
--mixed-precision "bf16" \
--epochs 240 \
--path-type "cosine" \
--loss-type "l2" \
--time-sampler "logit_normal" \
--time-mu -1.0 \
--time-sigma 1.4 \
--variational-adaptive-weight \
--no-debug
# run imm with class condition (IMM-cnd)
accelerate launch --multi_gpu train.py \
--exp-name "imm-b2-cnd" \
--output-dir "exp" \
--data-dir "../imagenet_vq/train" \
--model "SiT-B/2" \
--resolution 256 \
--batch-size 2048 \
--allow-tf32 \
--mixed-precision "fp16" \
--epochs 240 \
--path-type "linear" \
--time-sampler "logit_normal" \
--group-size 4 \
--gamma 12 \
--no-debug