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#!/usr/bin/env zsh
# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/home/kade/miniconda3/bin/conda' 'shell.zsh' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/home/kade/miniconda3/etc/profile.d/conda.sh" ]; then
. "/home/kade/miniconda3/etc/profile.d/conda.sh"
else
export PATH="/home/kade/miniconda3/bin:$PATH"
fi
fi
unset __conda_setup
# <<< conda initialize <<<
conda activate sdscripts
NAME="cotw-v1s400"
TRAINING_DIR="/home/kade/datasets/curse_of_the_worgen"
OUTPUT_DIR="/home/kade/output_dir"
# alpha=1 @ dim=16 is the same lr than alpha=4 @ dim=256
# --min_snr_gamma=1
args=(
# Model
--pretrained_model_name_or_path=/home/kade/ComfyUI/models/checkpoints/ponyDiffusionV6XL_v6StartWithThisOne.safetensors
# Keep Tokens
--keep_tokens=1
--keep_tokens_separator="|||"
# Output, logging
--output_dir="$OUTPUT_DIR/$NAME"
--output_name="$NAME"
--log_prefix="$NAME-"
--log_with=tensorboard
--logging_dir="$OUTPUT_DIR/logs"
--seed=1728871242
# Dataset
--train_data_dir="$TRAINING_DIR"
--dataset_repeats=1
--resolution="1024,1024"
--enable_bucket
--bucket_reso_steps=32
--min_bucket_reso=256
--max_bucket_reso=2048
--flip_aug
--shuffle_caption
--cache_latents
--cache_latents_to_disk
--max_data_loader_n_workers=8
--persistent_data_loader_workers
# Network config
--network_dim=8
--network_alpha=4
--network_module="lycoris.kohya"
--network_args
"preset=full"
"conv_dim=64"
"conv_alpha=2"
"rank_dropout=0"
"module_dropout=0"
"use_tucker=False"
"use_scalar=False"
"rank_dropout_scale=False"
"algo=lokr"
"dora_wd=True"
"train_norm=False"
--network_dropout=0
# Optimizer config
--optimizer_type=FCompass
--train_batch_size=12
--gradient_accumulation_steps=4
--max_grad_norm=1
--gradient_checkpointing
#--lr_warmup_steps=6
#--scale_weight_norms=1
# LR Scheduling
--max_train_steps=400
--learning_rate=0.0002
--unet_lr=0.0002
--text_encoder_lr=0.0001
--lr_scheduler="cosine"
--lr_scheduler_args="num_cycles=0.375"
# Noise
#--multires_noise_iterations=12
#--multires_noise_discount=0.4
#--min_snr_gamma=1
# Optimization, details
--no_half_vae
--sdpa
--mixed_precision="bf16"
# Saving
--save_model_as="safetensors"
--save_precision="fp16"
--save_every_n_steps=100
# Saving States
#--save_state
# Either resume from a saved state
#--resume="$OUTPUT_DIR/wolflink-vfucks400" # Resume from saved state
#--skip_until_initial_step
# Or from a checkpoint
#--network_weights="$OUTPUT_DIR/wolflink-vfucks400/wolflink-vfucks400-step00000120.safetensors" # Resume from checkpoint (not needed with state, i think)
#--initial_step=120
# Sampling
--sample_every_n_steps=100
--sample_prompts="$TRAINING_DIR/sample-prompts.txt"
--sample_sampler="euler_a"
--caption_extension=".txt"
)
cd ~/source/repos/sd-scripts
#accelerate launch --num_cpu_threads_per_process=2
python "./sdxl_train_network.py" "${args[@]}"
cd ~
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