training_scripts/tf-yyfriend

#!/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="yyfriend-v1s2000"
TRAINING_DIR="/home/kade/datasets/yyfriend"
OUTPUT_DIR="/home/kade/flux_output_dir/$NAME"

# 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 ~