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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:$PATH"
fi
fi
unset __conda_setup
# <<< conda initialize <
conda activate sdscripts
NAME="friend-v1s2000"
TRAINING_DIR="/home/kade/datasets/friend"
OUTPUT_DIR="/home/kade/flux_output_dir"
# Extract the number of steps from the NAME
STEPS=$(echo $NAME | grep -oE '[0-9]+$')
# If no number is found at the end of NAME, set a default value
if [ -z "$STEPS" ]; then
STEPS=4096
echo "No step count found in NAME. Using default value of \e[35m$STEPS\e[0m"
else
echo "Extracted \e[35m$STEPS\e[0m steps from NAME"
fi
args=(
## Model Paths
--pretrained_model_name_or_path ~/ComfyUI/models/unet/flux1-dev.safetensors
--clip_l ~/ComfyUI/models/clip/clip_l.safetensors
--t5xxl ~/ComfyUI/models/clip/t5xxl_fp16.safetensors
--ae ~/ComfyUI/models/vae/ae.safetensors
## Network Arguments
# NOTE: Bad idea to train T5!
#--network_args
# "train_t5xxl=True"
## Timestep Sampling
--timestep_sampling shift
# `--discrete_flow_shift` is the discrete flow shift for the Euler Discrete Scheduler,
# default is 3.0 (same as SD3).
--discrete_flow_shift 3.1582
# `--model_prediction_type` is how to interpret and process the model prediction.
# * `raw`: use as is, same as x-flux
# * `additive`: add to noisy input
# * `sigma_scaled`: apply sigma scaling, same as SD3
--model_prediction_type raw
--guidance_scale 1.0
# NOTE: In kohya's experiments,
# `--timestep_sampling shift --discrete_flow_shift 3.1582 --model_prediction_type raw --guidance_scale 1.0`
# (with the default `l2` `loss_type`) seems to work better.
#
# NOTE: The existing `--loss_type` option may be useful for FLUX.1 training. The default is `l2`.
#--loss_type l2
#
# Latents
--cache_latents_to_disk
--save_model_as safetensors
--sdpa
--persistent_data_loader_workers
--max_data_loader_n_workers 2
--seed 42
--max_train_steps=$STEPS
--gradient_checkpointing
--mixed_precision bf16
--optimizer_type=ClybW
--save_precision bf16
--network_module networks.lora_flux
--network_dim 4
--learning_rate 5e-4
--cache_text_encoder_outputs
--cache_text_encoder_outputs_to_disk
--fp8_base
--highvram
--dataset_config "$TRAINING_DIR/config.toml"
--output_dir $OUTPUT_DIR
--output_name $NAME
## Sample Prompts
--sample_prompts="$TRAINING_DIR/sample-prompts.txt"
--sample_every_n_steps=20
--sample_sampler="euler"
--sample_at_first
--save_every_n_steps=100
)
cd ~/source/repos/sd-scripts-sd3
python "./flux_train_network.py" "${args[@]}"
cd ~
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