Upload batch_lora_quickpod.sh

batch_lora_quickpod.sh

@@ -0,0 +1,48 @@
+#!/bin/bash
+
+input_file="/root/kohya_ss/remaining.txt"
+
+while IFS=' ' read -r word1 word2 word3 word4 word5; do
+    echo "test"
+    if [[ -z $word1 || -z $word2 || -z $word3 || -z $word4 || -z $word5 ]]; then
+        break
+    fi
+    echo "Processing $word3"
+    
+    # Change directory to the specific path, use quotes to handle spaces in filenames
+    cd /root/kohya_ss/dataset/instance/* || exit
+    rm -f * || exit  # Use -f to force removal and avoid prompts
+
+    wget "$word1" || exit
+    unzip -o *.zip || exit  # Use -o to overwrite files without prompting
+    rm -f *.zip || exit
+
+    # Loop through each text file in the directory
+    for file in ./*.txt; do
+        # Check if the file exists
+        if [[ -f "$file" ]]; then
+            # Use sed to remove the string and save the changes back to the file
+            sed -i "s/$word2//g" "$file"
+            echo "Processed file: $file"
+
+            # Remove leading spaces from each line in the file
+            sed -i 's/^ *//' "$file"
+            echo "Removed leading spaces from file: $file"
+        fi
+    done
+    file_count_png=$(ls -l /root/kohya_ss/dataset/instance/*/*.png | wc -l)
+    file_count_jpg=$(ls -l /root/kohya_ss/dataset/instance/*/*.jpg | wc -l)
+    file_count_jpeg=$(ls -l /root/kohya_ss/dataset/instance/*/*.jpeg| wc -l)
+    file_count=$((file_count_png + file_count_jpg + file_count_jpeg))
+    echo ${file_count}
+    # Run the Python script with debug output
+    cd /root/kohya_ss
+    echo "Starting Python script..."
+    accelerate launch --num_cpu_threads_per_process=8 "./sdxl_train_network.py" --enable_bucket --min_bucket_reso=256 --max_bucket_reso=2048 --pretrained_model_name_or_path="/root/kohya_ss/models/animagine-xl-3.1.safetensors" --train_data_dir="/root/kohya_ss/dataset/instance" --resolution="1280,1280" --output_dir="/root/kohya_ss/outputs" --logging_dir="/root/kohya_ss/logs" --network_alpha="48" --save_model_as=safetensors --network_module=networks.lora --network_args rank_dropout="0.1" module_dropout="0.1" --network_dim=96 --output_name=${word3} --lr_scheduler_num_cycles=$((word4)) --scale_weight_norms="5" --network_dropout="0.1" --no_half_vae --full_bf16 --learning_rate="8e-05" --lr_scheduler="constant" --train_batch_size="2" --max_train_steps=$((word4 * file_count)) --save_every_n_epochs=$((word5)) --mixed_precision="bf16" --save_precision="bf16" --seed="6969" --caption_extension=".txt" --cache_latents --optimizer_type="AdamW8bit" --max_grad_norm="1" --max_data_loader_n_workers="0" --max_token_length=225 --caption_dropout_rate="0.08" --bucket_reso_steps=64 --gradient_checkpointing --xformers --bucket_no_upscale --noise_offset=0.01 --adaptive_noise_scale=0.001
+    echo "Done Python script..."
+
+    cd /notebooks/kohya_ss || exit
+
+done < "$input_file"
+
+