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#!/bin/bash
# Define directories and files
TRAIN_DIR="benchmark_data/lora_format_test"
OUTPUT_EXP="/cm/archive/trilq3/exps/sota_comparisons/lora_sdxl"
EVAL_OUTPUT_DIR="/cm/archive/trilq3/output/sota_comparisons/lora_sdxl_fix_placeholder_fix_fix"
EVALUATION_PROMPT_FILE="benchmark_data/benchmark_prompts.json"
# Get list of directories and count them
FOLDERS=("$TRAIN_DIR"/*)
TOTAL_DIRS=${#FOLDERS[@]}
HALF=$((TOTAL_DIRS / 2))
echo "Processing second half: $HALF to $((TOTAL_DIRS-1))"
# Loop through second half of directories
for ((i = HALF; i < TOTAL_DIRS; i++)); do
folder="${FOLDERS[$i]}"
if [[ -d "$folder" ]]; then
echo "Processing: $folder"
FOLDER_NAME=$(basename "$folder")
INSTANCE_DATA_DIR="$folder/imgs"
PLACEHOLDER_STYLE=""
PLACEHOLDER_CONTENT=""
for type in "content" "style"; do
OUTPUT_DIR="$OUTPUT_EXP/$type/$FOLDER_NAME"
PROMPT_FILE="$folder/instance_prompt_$type.txt"
if [[ ! -f "$PROMPT_FILE" ]]; then
echo "Warning: Prompt file not found for $FOLDER_NAME, skipping..."
continue
fi
INSTANCE_PROMPT=$(<"$PROMPT_FILE")
echo "INSTANCE_PROMPT: $INSTANCE_PROMPT"
PLACEHOLDER=$(echo "$INSTANCE_PROMPT" | cut -d' ' -f2-)
echo "PLACEHOLDER: $PLACEHOLDER"
if [[ "$type" == "content" ]]; then
PLACEHOLDER_CONTENT="$PLACEHOLDER"
else
PLACEHOLDER_STYLE="$PLACEHOLDER"
fi
if [[ -d "$OUTPUT_DIR" ]]; then
echo "Output directory exists for $FOLDER_NAME, skipping training..."
continue
fi
mkdir -p "$OUTPUT_DIR"
CUDA_VISIBLE_DEVICES=3 accelerate launch --num_processes=1 --gpu_ids="all" train_dreambooth_lora_sdxl.py \
--pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
--instance_data_dir="$INSTANCE_DATA_DIR" \
--output_dir="$OUTPUT_DIR" \
--instance_prompt="$INSTANCE_PROMPT" \
--resolution=1024 \
--rank=64 \
--train_batch_size=1 \
--learning_rate=1e-4 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=200 \
--checkpointing_steps=200 \
--seed=0 \
--gradient_checkpointing \
--use_8bit_adam \
--mixed_precision="fp16" \
--enable_xformers_memory_efficient_attention
done
OUTPUT_RESULT_DIR="$EVAL_OUTPUT_DIR"
mkdir -p "$OUTPUT_RESULT_DIR"
start_time=$(date +%s)
CUDA_VISIBLE_DEVICES=3 python infer_lora.py \
--output_path="$OUTPUT_RESULT_DIR" \
--content_LoRA="$OUTPUT_EXP/content/$FOLDER_NAME" \
--content_alpha=1.0 \
--style_LoRA="$OUTPUT_EXP/style/$FOLDER_NAME" \
--style_alpha=1.0 \
--num_images_per_prompt=8 \
--name_concept="$FOLDER_NAME" \
--placeholder_style="$PLACEHOLDER_STYLE" \
--placeholder_content="$PLACEHOLDER_CONTENT" \
--evaluation_prompt_file="$EVALUATION_PROMPT_FILE"
end_time=$(date +%s)
echo "Time taken for inference: $((end_time - start_time)) seconds"
fi
done
# Run evaluation metrics script
python evaluate_with_metrics.py --result_dir "$EVAL_OUTPUT_DIR" --data_dir "$TRAIN_DIR"
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