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train_ipadapter.sh
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export CUDA_VISIBLE_DEVICES=0,1,2,3
task_flag="IP_Adapter" # the task flag is used to identify folders. # checkpoint root for resume
index_file=dataset/porcelain/jsons/porcelain_mt.json
results_dir=./log_EXP # save root for results
batch_size=1 # training batch size
image_size=1024 # training image resolution
grad_accu_steps=1 # gradient accumulation
warmup_num_steps=0 # warm-up steps
lr=0.0001 # learning rate
ckpt_every=10 # create a ckpt every a few steps.
ckpt_latest_every=10000 # create a ckpt named `latest.pt` every a few steps.
ckpt_every_n_epoch=2 # create a ckpt every a few epochs.
epochs=8 # total training epochs
PYTHONPATH=. \
sh ./hydit/run_g_ipadapter.sh \
--task-flag ${task_flag} \
--noise-schedule scaled_linear --beta-start 0.00085 --beta-end 0.018 \
--predict-type v_prediction \
--multireso \
--reso-step 64 \
--uncond-p 0.22 \
--uncond-p-t5 0.22\
--uncond-p-img 0.05\
--index-file ${index_file} \
--random-flip \
--lr ${lr} \
--batch-size ${batch_size} \
--image-size ${image_size} \
--global-seed 999 \
--grad-accu-steps ${grad_accu_steps} \
--warmup-num-steps ${warmup_num_steps} \
--use-flash-attn \
--use-fp16 \
--extra-fp16 \
--results-dir ${results_dir} \
--resume\
--resume-module-root ./ckpts/t2i/model/pytorch_model_distill.pt \
--epochs ${epochs} \
--ckpt-every ${ckpt_every} \
--ckpt-latest-every ${ckpt_latest_every} \
--ckpt-every-n-epoch ${ckpt_every_n_epoch} \
--log-every 10 \
--deepspeed \
--use-zero-stage 2 \
--gradient-checkpointing \
--no-strict \
--training-parts ipadapter \
--is-ipa True \
--resume-ipa True \
--resume-ipa-root ./ckpts/t2i/model/ipa.pt \
"$@"