Create train_lora_p1.sh

train_lora_p1.sh

@@ -0,0 +1,99 @@
+#!/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 first half: 0 to $((HALF-1))"
+
+# Loop through first half of directories
+for ((i = 0; i < HALF; i++)); do
+    folder="${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=0 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=0 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"