Fix issue with lora model loading

finetune/prepare_buckets_latents_new.py

@@ -0,0 +1,180 @@
+# このスクリプトのライセンスは、Apache License 2.0とします
+# (c) 2022 Kohya S. @kohya_ss
+
+import argparse
+import glob
+import os
+import json
+
+from tqdm import tqdm
+import numpy as np
+from diffusers import AutoencoderKL
+from PIL import Image
+import cv2
+import torch
+from torchvision import transforms
+
+import library.model_util as model_util
+
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+IMAGE_TRANSFORMS = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.5], [0.5]),
+    ]
+)
+
+
+def get_latents(vae, images, weight_dtype):
+  img_tensors = [IMAGE_TRANSFORMS(image) for image in images]
+  img_tensors = torch.stack(img_tensors)
+  img_tensors = img_tensors.to(DEVICE, weight_dtype)
+  with torch.no_grad():
+    latents = vae.encode(img_tensors).latent_dist.sample().float().to("cpu").numpy()
+  return latents
+
+
+def main(args):
+  # image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
+  #     glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
+  # print(f"found {len(image_paths)} images.")
+  
+
+  if os.path.exists(args.in_json):
+    print(f"loading existing metadata: {args.in_json}")
+    with open(args.in_json, "rt", encoding='utf-8') as f:
+      metadata = json.load(f)
+  else:
+    print(f"no metadata / メタデータファイルがありません: {args.in_json}")
+    return
+
+  weight_dtype = torch.float32
+  if args.mixed_precision == "fp16":
+    weight_dtype = torch.float16
+  elif args.mixed_precision == "bf16":
+    weight_dtype = torch.bfloat16
+
+  vae = model_util.load_vae(args.model_name_or_path, weight_dtype)
+  vae.eval()
+  vae.to(DEVICE, dtype=weight_dtype)
+
+  # bucketのサイズを計算する
+  max_reso = tuple([int(t) for t in args.max_resolution.split(',')])
+  assert len(max_reso) == 2, f"illegal resolution (not 'width,height') / 画像サイズに誤りがあります。'幅,高さ'で指定してください: {args.max_resolution}"
+
+  bucket_resos, bucket_aspect_ratios = model_util.make_bucket_resolutions(
+      max_reso, args.min_bucket_reso, args.max_bucket_reso)
+
+  # 画像をひとつずつ適切なbucketに割り当てながらlatentを計算する
+  bucket_aspect_ratios = np.array(bucket_aspect_ratios)
+  buckets_imgs = [[] for _ in range(len(bucket_resos))]
+  bucket_counts = [0 for _ in range(len(bucket_resos))]
+  img_ar_errors = []
+  for i, image_path in enumerate(tqdm(metadata, smoothing=0.0)):
+    image_key = image_path
+    if image_key not in metadata:
+      metadata[image_key] = {}
+
+    image = Image.open(image_path)
+    if image.mode != 'RGB':
+      image = image.convert("RGB")
+
+    aspect_ratio = image.width / image.height
+    ar_errors = bucket_aspect_ratios - aspect_ratio
+    bucket_id = np.abs(ar_errors).argmin()
+    reso = bucket_resos[bucket_id]
+    ar_error = ar_errors[bucket_id]
+    img_ar_errors.append(abs(ar_error))
+
+    # どのサイズにリサイズするか→トリミングする方向で
+    if ar_error <= 0:                   # 横が長い→縦を合わせる
+      scale = reso[1] / image.height
+    else:
+      scale = reso[0] / image.width
+
+    resized_size = (int(image.width * scale + .5), int(image.height * scale + .5))
+
+    # print(image.width, image.height, bucket_id, bucket_resos[bucket_id], ar_errors[bucket_id], resized_size,
+    #       bucket_resos[bucket_id][0] - resized_size[0], bucket_resos[bucket_id][1] - resized_size[1])
+
+    assert resized_size[0] == reso[0] or resized_size[1] == reso[
+        1], f"internal error, resized size not match: {reso}, {resized_size}, {image.width}, {image.height}"
+    assert resized_size[0] >= reso[0] and resized_size[1] >= reso[
+        1], f"internal error, resized size too small: {reso}, {resized_size}, {image.width}, {image.height}"
+
+    # 画像をリサイズしてトリミングする
+    # PILにinter_areaがないのでcv2で……
+    image = np.array(image)
+    image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)
+    if resized_size[0] > reso[0]:
+      trim_size = resized_size[0] - reso[0]
+      image = image[:, trim_size//2:trim_size//2 + reso[0]]
+    elif resized_size[1] > reso[1]:
+      trim_size = resized_size[1] - reso[1]
+      image = image[trim_size//2:trim_size//2 + reso[1]]
+    assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}"
+
+    # # debug
+    # cv2.imwrite(f"r:\\test\\img_{i:05d}.jpg", image[:, :, ::-1])
+
+    # バッチへ追加
+    buckets_imgs[bucket_id].append((image_key, reso, image))
+    bucket_counts[bucket_id] += 1
+    metadata[image_key]['train_resolution'] = reso
+
+    # バッチを推論するか判定して推論する
+    is_last = i == len(metadata) - 1
+    for j in range(len(buckets_imgs)):
+      bucket = buckets_imgs[j]
+      if (is_last and len(bucket) > 0) or len(bucket) >= args.batch_size:
+        latents = get_latents(vae, [img for _, _, img in bucket], weight_dtype)
+
+        for (image_key, reso, _), latent in zip(bucket, latents):
+          npz_file_name = os.path.splitext(os.path.basename(image_key))[0]
+          np.savez(os.path.join(os.path.dirname(image_key), npz_file_name), latent)
+
+        # flip
+        if args.flip_aug:
+          latents = get_latents(vae, [img[:, ::-1].copy() for _, _, img in bucket], weight_dtype)   # copyがないとTensor変換できない
+
+          for (image_key, reso, _), latent in zip(bucket, latents):
+            npz_file_name = os.path.splitext(os.path.basename(image_key))[0]
+            np.savez(os.path.join(os.path.dirname(image_key), npz_file_name + '_flip'), latent)
+
+        bucket.clear()
+
+  for i, (reso, count) in enumerate(zip(bucket_resos, bucket_counts)):
+    print(f"bucket {i} {reso}: {count}")
+  img_ar_errors = np.array(img_ar_errors)
+  print(f"mean ar error: {np.mean(img_ar_errors)}")
+
+  # metadataを書き出して終わり
+  print(f"writing metadata: {args.out_json}")
+  with open(args.out_json, "wt", encoding='utf-8') as f:
+    json.dump(metadata, f, indent=2)
+  print("done!")
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
+  parser.add_argument("in_json", type=str, help="metadata file to input / 読み込むメタデータファイル")
+  parser.add_argument("out_json", type=str, help="metadata file to output / メタデータファイル書き出し先")
+  parser.add_argument("model_name_or_path", type=str, help="model name or path to encode latents / latentを取得するためのモデル")
+  parser.add_argument("--v2", action='store_true',
+                      help='load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む')
+  parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+  parser.add_argument("--max_resolution", type=str, default="512,512",
+                      help="max resolution in fine tuning (width,height) / fine tuning時の最大画像サイズ 「幅,高さ」（使用メモリ量に関係します）")
+  parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度")
+  parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最小解像度")
+  parser.add_argument("--mixed_precision", type=str, default="no",
+                      choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度")
+  parser.add_argument("--full_path", action="store_true",
+                      help="use full path as image-key in metadata (supports multiple directories) / メタデータで画像キーをフルパスにする（複数の学習画像ディレクトリに対応）")
+  parser.add_argument("--flip_aug", action="store_true",
+                      help="flip augmentation, save latents for flipped images / 左右反転した画像もlatentを取得、保存する")
+
+  args = parser.parse_args()
+  main(args)

finetune_gui copy.py

@@ -0,0 +1,789 @@
+import gradio as gr
+import json
+import math
+import os
+import subprocess
+import pathlib
+import shutil
+import argparse
+from library.common_gui import (
+    get_folder_path,
+    get_file_path,
+    get_any_file_path,
+    get_saveasfile_path,
+)
+from library.utilities import utilities_tab
+
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+document_symbol = '\U0001F4C4'   # 📄
+
+
+def save_configuration(
+    save_as,
+    file_path,
+    pretrained_model_name_or_path,
+    v2,
+    v_parameterization,
+    train_dir,
+    image_folder,
+    output_dir,
+    logging_dir,
+    max_resolution,
+    min_bucket_reso,
+    max_bucket_reso,
+    batch_size,
+    flip_aug,
+    caption_metadata_filename,
+    latent_metadata_filename,
+    full_path,
+    learning_rate,
+    lr_scheduler,
+    lr_warmup,
+    dataset_repeats,
+    train_batch_size,
+    epoch,
+    save_every_n_epochs,
+    mixed_precision,
+    save_precision,
+    seed,
+    num_cpu_threads_per_process,
+    train_text_encoder,
+    create_caption,
+    create_buckets,
+    save_model_as,
+    caption_extension,
+    use_8bit_adam,
+    xformers,
+    clip_skip,
+):
+    original_file_path = file_path
+
+    save_as_bool = True if save_as.get('label') == 'True' else False
+
+    if save_as_bool:
+        print('Save as...')
+        file_path = get_saveasfile_path(file_path)
+    else:
+        print('Save...')
+        if file_path == None or file_path == '':
+            file_path = get_saveasfile_path(file_path)
+
+    # print(file_path)
+
+    if file_path == None:
+        return original_file_path
+
+    # Return the values of the variables as a dictionary
+    variables = {
+        'pretrained_model_name_or_path': pretrained_model_name_or_path,
+        'v2': v2,
+        'v_parameterization': v_parameterization,
+        'train_dir': train_dir,
+        'image_folder': image_folder,
+        'output_dir': output_dir,
+        'logging_dir': logging_dir,
+        'max_resolution': max_resolution,
+        'min_bucket_reso': min_bucket_reso,
+        'max_bucket_reso': max_bucket_reso,
+        'batch_size': batch_size,
+        'flip_aug': flip_aug,
+        'caption_metadata_filename': caption_metadata_filename,
+        'latent_metadata_filename': latent_metadata_filename,
+        'full_path': full_path,
+        'learning_rate': learning_rate,
+        'lr_scheduler': lr_scheduler,
+        'lr_warmup': lr_warmup,
+        'dataset_repeats': dataset_repeats,
+        'train_batch_size': train_batch_size,
+        'epoch': epoch,
+        'save_every_n_epochs': save_every_n_epochs,
+        'mixed_precision': mixed_precision,
+        'save_precision': save_precision,
+        'seed': seed,
+        'num_cpu_threads_per_process': num_cpu_threads_per_process,
+        'train_text_encoder': train_text_encoder,
+        'create_buckets': create_buckets,
+        'create_caption': create_caption,
+        'save_model_as': save_model_as,
+        'caption_extension': caption_extension,
+        'use_8bit_adam': use_8bit_adam,
+        'xformers': xformers,
+        'clip_skip': clip_skip,
+    }
+
+    # Save the data to the selected file
+    with open(file_path, 'w') as file:
+        json.dump(variables, file)
+
+    return file_path
+
+
+def open_config_file(
+    file_path,
+    pretrained_model_name_or_path,
+    v2,
+    v_parameterization,
+    train_dir,
+    image_folder,
+    output_dir,
+    logging_dir,
+    max_resolution,
+    min_bucket_reso,
+    max_bucket_reso,
+    batch_size,
+    flip_aug,
+    caption_metadata_filename,
+    latent_metadata_filename,
+    full_path,
+    learning_rate,
+    lr_scheduler,
+    lr_warmup,
+    dataset_repeats,
+    train_batch_size,
+    epoch,
+    save_every_n_epochs,
+    mixed_precision,
+    save_precision,
+    seed,
+    num_cpu_threads_per_process,
+    train_text_encoder,
+    create_caption,
+    create_buckets,
+    save_model_as,
+    caption_extension,
+    use_8bit_adam,
+    xformers,
+    clip_skip,
+):
+    original_file_path = file_path
+    file_path = get_file_path(file_path)
+
+    if file_path != '' and file_path != None:
+        print(file_path)
+        # load variables from JSON file
+        with open(file_path, 'r') as f:
+            my_data = json.load(f)
+    else:
+        file_path = original_file_path   # In case a file_path was provided and the user decide to cancel the open action
+        my_data = {}
+
+    # Return the values of the variables as a dictionary
+    return (
+        file_path,
+        my_data.get(
+            'pretrained_model_name_or_path', pretrained_model_name_or_path
+        ),
+        my_data.get('v2', v2),
+        my_data.get('v_parameterization', v_parameterization),
+        my_data.get('train_dir', train_dir),
+        my_data.get('image_folder', image_folder),
+        my_data.get('output_dir', output_dir),
+        my_data.get('logging_dir', logging_dir),
+        my_data.get('max_resolution', max_resolution),
+        my_data.get('min_bucket_reso', min_bucket_reso),
+        my_data.get('max_bucket_reso', max_bucket_reso),
+        my_data.get('batch_size', batch_size),
+        my_data.get('flip_aug', flip_aug),
+        my_data.get('caption_metadata_filename', caption_metadata_filename),
+        my_data.get('latent_metadata_filename', latent_metadata_filename),
+        my_data.get('full_path', full_path),
+        my_data.get('learning_rate', learning_rate),
+        my_data.get('lr_scheduler', lr_scheduler),
+        my_data.get('lr_warmup', lr_warmup),
+        my_data.get('dataset_repeats', dataset_repeats),
+        my_data.get('train_batch_size', train_batch_size),
+        my_data.get('epoch', epoch),
+        my_data.get('save_every_n_epochs', save_every_n_epochs),
+        my_data.get('mixed_precision', mixed_precision),
+        my_data.get('save_precision', save_precision),
+        my_data.get('seed', seed),
+        my_data.get(
+            'num_cpu_threads_per_process', num_cpu_threads_per_process
+        ),
+        my_data.get('train_text_encoder', train_text_encoder),
+        my_data.get('create_buckets', create_buckets),
+        my_data.get('create_caption', create_caption),
+        my_data.get('save_model_as', save_model_as),
+        my_data.get('caption_extension', caption_extension),
+        my_data.get('use_8bit_adam', use_8bit_adam),
+        my_data.get('xformers', xformers),
+        my_data.get('clip_skip', clip_skip),
+    )
+
+
+def train_model(
+    pretrained_model_name_or_path,
+    v2,
+    v_parameterization,
+    train_dir,
+    image_folder,
+    output_dir,
+    logging_dir,
+    max_resolution,
+    min_bucket_reso,
+    max_bucket_reso,
+    batch_size,
+    flip_aug,
+    caption_metadata_filename,
+    latent_metadata_filename,
+    full_path,
+    learning_rate,
+    lr_scheduler,
+    lr_warmup,
+    dataset_repeats,
+    train_batch_size,
+    epoch,
+    save_every_n_epochs,
+    mixed_precision,
+    save_precision,
+    seed,
+    num_cpu_threads_per_process,
+    train_text_encoder,
+    generate_caption_database,
+    generate_image_buckets,
+    save_model_as,
+    caption_extension,
+    use_8bit_adam,
+    xformers,
+    clip_skip,
+):
+    def save_inference_file(output_dir, v2, v_parameterization):
+        # Copy inference model for v2 if required
+        if v2 and v_parameterization:
+            print(f'Saving v2-inference-v.yaml as {output_dir}/last.yaml')
+            shutil.copy(
+                f'./v2_inference/v2-inference-v.yaml',
+                f'{output_dir}/last.yaml',
+            )
+        elif v2:
+            print(f'Saving v2-inference.yaml as {output_dir}/last.yaml')
+            shutil.copy(
+                f'./v2_inference/v2-inference.yaml',
+                f'{output_dir}/last.yaml',
+            )
+
+    # create caption json file
+    if generate_caption_database:
+        if not os.path.exists(train_dir):
+            os.mkdir(train_dir)
+
+        for root, dirs, files in os.walk(image_folder):
+            for dir in dirs:
+                print(os.path.join(root, dir))
+
+                run_cmd = (
+                    f'./venv/Scripts/python.exe finetune/merge_captions_to_metadata.py'
+                )
+                if caption_extension == '':
+                    run_cmd += f' --caption_extension=".txt"'
+                else:
+                    run_cmd += f' --caption_extension={caption_extension}'
+                run_cmd += f' "{os.path.join(root, dir)}"'
+                run_cmd += f' "{train_dir}/{caption_metadata_filename}"'
+                if full_path:
+                    run_cmd += f' --full_path'
+
+                print(run_cmd)
+
+                # Run the command
+                subprocess.run(run_cmd)
+
+    # create images buckets
+    if generate_image_buckets:
+        run_cmd = (
+            f'./venv/Scripts/python.exe finetune/prepare_buckets_latents.py'
+        )
+        run_cmd += f' "crap"'
+        run_cmd += f' "{train_dir}/{caption_metadata_filename}"'
+        run_cmd += f' "{train_dir}/{latent_metadata_filename}"'
+        run_cmd += f' "{pretrained_model_name_or_path}"'
+        run_cmd += f' --batch_size={batch_size}'
+        run_cmd += f' --max_resolution={max_resolution}'
+        run_cmd += f' --min_bucket_reso={min_bucket_reso}'
+        run_cmd += f' --max_bucket_reso={max_bucket_reso}'
+        run_cmd += f' --mixed_precision={mixed_precision}'
+        if flip_aug:
+            run_cmd += f' --flip_aug'
+        if full_path:
+            run_cmd += f' --full_path'
+
+        print(run_cmd)
+
+        # Run the command
+        subprocess.run(run_cmd)
+
+    image_num = 0
+    for root, dirs, files in os.walk(image_folder):
+        for dir in dirs:
+            image_num += len(
+                [f for f in os.listdir(os.path.join(root, dir)) if f.endswith('.npz')]
+            )
+    print(f'image_num = {image_num}')
+
+    repeats = int(image_num) * int(dataset_repeats)
+    print(f'repeats = {str(repeats)}')
+
+    # calculate max_train_steps
+    max_train_steps = int(
+        math.ceil(float(repeats) / int(train_batch_size) * int(epoch))
+    )
+
+    # Divide by two because flip augmentation create two copied of the source images
+    if flip_aug:
+        max_train_steps = int(math.ceil(float(max_train_steps) / 2))
+
+    print(f'max_train_steps = {max_train_steps}')
+
+    lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100))
+    print(f'lr_warmup_steps = {lr_warmup_steps}')
+
+    run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "./fine_tune.py"'
+    if v2:
+        run_cmd += ' --v2'
+    if v_parameterization:
+        run_cmd += ' --v_parameterization'
+    if train_text_encoder:
+        run_cmd += ' --train_text_encoder'
+    if use_8bit_adam:
+        run_cmd += f' --use_8bit_adam'
+    if xformers:
+        run_cmd += f' --xformers'
+    run_cmd += (
+        f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
+    )
+    run_cmd += f' --in_json="{train_dir}/{latent_metadata_filename}"'
+    run_cmd += f' --train_data_dir="{image_folder}"'
+    run_cmd += f' --output_dir="{output_dir}"'
+    if not logging_dir == '':
+        run_cmd += f' --logging_dir="{logging_dir}"'
+    run_cmd += f' --train_batch_size={train_batch_size}'
+    run_cmd += f' --dataset_repeats={dataset_repeats}'
+    run_cmd += f' --learning_rate={learning_rate}'
+    run_cmd += f' --lr_scheduler={lr_scheduler}'
+    run_cmd += f' --lr_warmup_steps={lr_warmup_steps}'
+    run_cmd += f' --max_train_steps={max_train_steps}'
+    run_cmd += f' --mixed_precision={mixed_precision}'
+    run_cmd += f' --save_every_n_epochs={save_every_n_epochs}'
+    run_cmd += f' --seed={seed}'
+    run_cmd += f' --save_precision={save_precision}'
+    if not save_model_as == 'same as source model':
+        run_cmd += f' --save_model_as={save_model_as}'
+    if int(clip_skip) > 1:
+        run_cmd += f' --clip_skip={str(clip_skip)}'
+
+    print(run_cmd)
+    # Run the command
+    subprocess.run(run_cmd)
+
+    # check if output_dir/last is a folder... therefore it is a diffuser model
+    last_dir = pathlib.Path(f'{output_dir}/last')
+
+    if not last_dir.is_dir():
+        # Copy inference model for v2 if required
+        save_inference_file(output_dir, v2, v_parameterization)
+
+
+def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
+    # define a list of substrings to search for
+    substrings_v2 = [
+        'stabilityai/stable-diffusion-2-1-base',
+        'stabilityai/stable-diffusion-2-base',
+    ]
+
+    # check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
+    if str(value) in substrings_v2:
+        print('SD v2 model detected. Setting --v2 parameter')
+        v2 = True
+        v_parameterization = False
+
+        return value, v2, v_parameterization
+
+    # define a list of substrings to search for v-objective
+    substrings_v_parameterization = [
+        'stabilityai/stable-diffusion-2-1',
+        'stabilityai/stable-diffusion-2',
+    ]
+
+    # check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
+    if str(value) in substrings_v_parameterization:
+        print(
+            'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
+        )
+        v2 = True
+        v_parameterization = True
+
+        return value, v2, v_parameterization
+
+    # define a list of substrings to v1.x
+    substrings_v1_model = [
+        'CompVis/stable-diffusion-v1-4',
+        'runwayml/stable-diffusion-v1-5',
+    ]
+
+    if str(value) in substrings_v1_model:
+        v2 = False
+        v_parameterization = False
+
+        return value, v2, v_parameterization
+
+    if value == 'custom':
+        value = ''
+        v2 = False
+        v_parameterization = False
+
+        return value, v2, v_parameterization
+
+
+def remove_doublequote(file_path):
+    if file_path != None:
+        file_path = file_path.replace('"', '')
+
+    return file_path
+
+
+def UI(username, password):
+
+    css = ''
+
+    if os.path.exists('./style.css'):
+        with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
+            print('Load CSS...')
+            css += file.read() + '\n'
+
+    interface = gr.Blocks(css=css)
+
+    with interface:
+        with gr.Tab('Finetune'):
+            finetune_tab()
+        with gr.Tab('Utilities'):
+            utilities_tab(enable_dreambooth_tab=False)
+
+    # Show the interface
+    if not username == '':
+        interface.launch(auth=(username, password))
+    else:
+        interface.launch()
+
+
+def finetune_tab():
+    dummy_ft_true = gr.Label(value=True, visible=False)
+    dummy_ft_false = gr.Label(value=False, visible=False)
+    gr.Markdown('Train a custom model using kohya finetune python code...')
+    with gr.Accordion('Configuration file', open=False):
+        with gr.Row():
+            button_open_config = gr.Button(
+                f'Open {folder_symbol}', elem_id='open_folder'
+            )
+            button_save_config = gr.Button(
+                f'Save {save_style_symbol}', elem_id='open_folder'
+            )
+            button_save_as_config = gr.Button(
+                f'Save as... {save_style_symbol}',
+                elem_id='open_folder',
+            )
+        config_file_name = gr.Textbox(
+            label='', placeholder='type file path or use buttons...'
+        )
+        config_file_name.change(
+            remove_doublequote,
+            inputs=[config_file_name],
+            outputs=[config_file_name],
+        )
+    with gr.Tab('Source model'):
+        # Define the input elements
+        with gr.Row():
+            pretrained_model_name_or_path_input = gr.Textbox(
+                label='Pretrained model name or path',
+                placeholder='enter the path to custom model or name of pretrained model',
+            )
+            pretrained_model_name_or_path_file = gr.Button(
+                document_symbol, elem_id='open_folder_small'
+            )
+            pretrained_model_name_or_path_file.click(
+                get_any_file_path,
+                inputs=pretrained_model_name_or_path_input,
+                outputs=pretrained_model_name_or_path_input,
+            )
+            pretrained_model_name_or_path_folder = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            pretrained_model_name_or_path_folder.click(
+                get_folder_path,
+                inputs=pretrained_model_name_or_path_input,
+                outputs=pretrained_model_name_or_path_input,
+            )
+            model_list = gr.Dropdown(
+                label='(Optional) Model Quick Pick',
+                choices=[
+                    'custom',
+                    'stabilityai/stable-diffusion-2-1-base',
+                    'stabilityai/stable-diffusion-2-base',
+                    'stabilityai/stable-diffusion-2-1',
+                    'stabilityai/stable-diffusion-2',
+                    'runwayml/stable-diffusion-v1-5',
+                    'CompVis/stable-diffusion-v1-4',
+                ],
+            )
+            save_model_as_dropdown = gr.Dropdown(
+                label='Save trained model as',
+                choices=[
+                    'same as source model',
+                    'ckpt',
+                    'diffusers',
+                    'diffusers_safetensors',
+                    'safetensors',
+                ],
+                value='same as source model',
+            )
+
+        with gr.Row():
+            v2_input = gr.Checkbox(label='v2', value=True)
+            v_parameterization_input = gr.Checkbox(
+                label='v_parameterization', value=False
+            )
+        model_list.change(
+            set_pretrained_model_name_or_path_input,
+            inputs=[model_list, v2_input, v_parameterization_input],
+            outputs=[
+                pretrained_model_name_or_path_input,
+                v2_input,
+                v_parameterization_input,
+            ],
+        )
+    with gr.Tab('Folders'):
+        with gr.Row():
+            train_dir_input = gr.Textbox(
+                label='Training config folder',
+                placeholder='folder where the training configuration files will be saved',
+            )
+            train_dir_folder = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            train_dir_folder.click(get_folder_path, outputs=train_dir_input)
+
+            image_folder_input = gr.Textbox(
+                label='Training Image folder',
+                placeholder='folder where the training images are located',
+            )
+            image_folder_input_folder = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            image_folder_input_folder.click(
+                get_folder_path, outputs=image_folder_input
+            )
+        with gr.Row():
+            output_dir_input = gr.Textbox(
+                label='Output folder',
+                placeholder='folder where the model will be saved',
+            )
+            output_dir_input_folder = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            output_dir_input_folder.click(
+                get_folder_path, outputs=output_dir_input
+            )
+
+            logging_dir_input = gr.Textbox(
+                label='Logging folder',
+                placeholder='Optional: enable logging and output TensorBoard log to this folder',
+            )
+            logging_dir_input_folder = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            logging_dir_input_folder.click(
+                get_folder_path, outputs=logging_dir_input
+            )
+        train_dir_input.change(
+            remove_doublequote,
+            inputs=[train_dir_input],
+            outputs=[train_dir_input],
+        )
+        image_folder_input.change(
+            remove_doublequote,
+            inputs=[image_folder_input],
+            outputs=[image_folder_input],
+        )
+        output_dir_input.change(
+            remove_doublequote,
+            inputs=[output_dir_input],
+            outputs=[output_dir_input],
+        )
+    with gr.Tab('Dataset preparation'):
+        with gr.Row():
+            max_resolution_input = gr.Textbox(
+                label='Resolution (width,height)', value='512,512'
+            )
+            min_bucket_reso = gr.Textbox(
+                label='Min bucket resolution', value='256'
+            )
+            max_bucket_reso = gr.Textbox(
+                label='Max bucket resolution', value='1024'
+            )
+            batch_size = gr.Textbox(label='Batch size', value='1')
+        with gr.Accordion('Advanced parameters', open=False):
+            with gr.Row():
+                caption_metadata_filename = gr.Textbox(
+                    label='Caption metadata filename', value='meta_cap.json'
+                )
+                latent_metadata_filename = gr.Textbox(
+                    label='Latent metadata filename', value='meta_lat.json'
+                )
+                full_path = gr.Checkbox(label='Use full path', value=True)
+                flip_aug = gr.Checkbox(label='Flip augmentation', value=False)
+    with gr.Tab('Training parameters'):
+        with gr.Row():
+            learning_rate_input = gr.Textbox(label='Learning rate', value=1e-6)
+            lr_scheduler_input = gr.Dropdown(
+                label='LR Scheduler',
+                choices=[
+                    'constant',
+                    'constant_with_warmup',
+                    'cosine',
+                    'cosine_with_restarts',
+                    'linear',
+                    'polynomial',
+                ],
+                value='constant',
+            )
+            lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
+        with gr.Row():
+            dataset_repeats_input = gr.Textbox(
+                label='Dataset repeats', value=40
+            )
+            train_batch_size_input = gr.Slider(
+                minimum=1,
+                maximum=32,
+                label='Train batch size',
+                value=1,
+                step=1,
+            )
+            epoch_input = gr.Textbox(label='Epoch', value=1)
+            save_every_n_epochs_input = gr.Textbox(
+                label='Save every N epochs', value=1
+            )
+        with gr.Row():
+            mixed_precision_input = gr.Dropdown(
+                label='Mixed precision',
+                choices=[
+                    'no',
+                    'fp16',
+                    'bf16',
+                ],
+                value='fp16',
+            )
+            save_precision_input = gr.Dropdown(
+                label='Save precision',
+                choices=[
+                    'float',
+                    'fp16',
+                    'bf16',
+                ],
+                value='fp16',
+            )
+            num_cpu_threads_per_process_input = gr.Slider(
+                minimum=1,
+                maximum=os.cpu_count(),
+                step=1,
+                label='Number of CPU threads per process',
+                value=os.cpu_count(),
+            )
+            seed_input = gr.Textbox(label='Seed', value=1234)
+        with gr.Row():
+            caption_extention_input = gr.Textbox(
+                label='Caption Extension',
+                placeholder='(Optional) Extension for caption files. default: .txt',
+            )
+            train_text_encoder_input = gr.Checkbox(
+                label='Train text encoder', value=True
+            )
+        with gr.Accordion('Advanced parameters', open=False):
+            with gr.Row():
+                use_8bit_adam = gr.Checkbox(label='Use 8bit adam', value=True)
+                xformers = gr.Checkbox(label='Use xformers', value=True)
+                clip_skip = gr.Slider(
+                    label='Clip skip', value='1', minimum=1, maximum=12, step=1
+                )
+    with gr.Box():
+        with gr.Row():
+            create_caption = gr.Checkbox(
+                label='Generate caption metadata', value=True
+            )
+            create_buckets = gr.Checkbox(
+                label='Generate image buckets metadata', value=True
+            )
+
+    button_run = gr.Button('Train model')
+
+    settings_list = [
+        pretrained_model_name_or_path_input,
+        v2_input,
+        v_parameterization_input,
+        train_dir_input,
+        image_folder_input,
+        output_dir_input,
+        logging_dir_input,
+        max_resolution_input,
+        min_bucket_reso,
+        max_bucket_reso,
+        batch_size,
+        flip_aug,
+        caption_metadata_filename,
+        latent_metadata_filename,
+        full_path,
+        learning_rate_input,
+        lr_scheduler_input,
+        lr_warmup_input,
+        dataset_repeats_input,
+        train_batch_size_input,
+        epoch_input,
+        save_every_n_epochs_input,
+        mixed_precision_input,
+        save_precision_input,
+        seed_input,
+        num_cpu_threads_per_process_input,
+        train_text_encoder_input,
+        create_caption,
+        create_buckets,
+        save_model_as_dropdown,
+        caption_extention_input,
+        use_8bit_adam,
+        xformers,
+        clip_skip,
+    ]
+
+    button_run.click(train_model, inputs=settings_list)
+
+    button_open_config.click(
+        open_config_file,
+        inputs=[config_file_name] + settings_list,
+        outputs=[config_file_name] + settings_list,
+    )
+
+    button_save_config.click(
+        save_configuration,
+        inputs=[dummy_ft_false, config_file_name] + settings_list,
+        outputs=[config_file_name],
+    )
+
+    button_save_as_config.click(
+        save_configuration,
+        inputs=[dummy_ft_true, config_file_name] + settings_list,
+        outputs=[config_file_name],
+    )
+
+
+if __name__ == '__main__':
+    # torch.cuda.set_per_process_memory_fraction(0.48)
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--username', type=str, default='', help='Username for authentication'
+    )
+    parser.add_argument(
+        '--password', type=str, default='', help='Password for authentication'
+    )
+
+    args = parser.parse_args()
+
+    UI(username=args.username, password=args.password)

lora_gui.py

@@ -79,6 +79,7 @@ def save_configuration(
     gradient_accumulation_steps,
     mem_eff_attn,
     output_name,
+    model_list,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -120,32 +121,32 @@ def save_configuration(
 
 def open_configuration(
     file_path,
-    pretrained_model_name_or_path_input,
-    v2_input,
-    v_parameterization_input,
-    logging_dir_input,
-    train_data_dir_input,
-    reg_data_dir_input,
-    output_dir_input,
-    max_resolution_input,
-    lr_scheduler_input,
-    lr_warmup_input,
-    train_batch_size_input,
-    epoch_input,
-    save_every_n_epochs_input,
-    mixed_precision_input,
-    save_precision_input,
-    seed_input,
-    num_cpu_threads_per_process_input,
-    cache_latent_input,
-    caption_extention_input,
-    enable_bucket_input,
+    pretrained_model_name_or_path,
+    v2,
+    v_parameterization,
+    logging_dir,
+    train_data_dir,
+    reg_data_dir,
+    output_dir,
+    max_resolution,
+    lr_scheduler,
+    lr_warmup,
+    train_batch_size,
+    epoch,
+    save_every_n_epochs,
+    mixed_precision,
+    save_precision,
+    seed,
+    num_cpu_threads_per_process,
+    cache_latent,
+    caption_extention,
+    enable_bucket,
     gradient_checkpointing,
-    full_fp16_input,
-    no_token_padding_input,
-    stop_text_encoder_training_input,
-    use_8bit_adam_input,
-    xformers_input,
+    full_fp16,
+    no_token_padding,
+    stop_text_encoder_training,
+    use_8bit_adam,
+    xformers,
     save_model_as_dropdown,
     shuffle_caption,
     save_state,
@@ -161,27 +162,28 @@ def open_configuration(
     gradient_accumulation_steps,
     mem_eff_attn,
     output_name,
+    model_list,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
-
+    
     original_file_path = file_path
     file_path = get_file_path(file_path)
 
     if not file_path == '' and not file_path == None:
         # load variables from JSON file
         with open(file_path, 'r') as f:
-            my_data_lora = json.load(f)
+            my_data = json.load(f)
             print("Loading config...")
     else:
         file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
-        my_data_lora = {}
-    
+        my_data = {}
+            
     values = [file_path]
     for key, value in parameters:
         # Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
         if not key in ['file_path']:
-            values.append(my_data_lora.get(key, value))
+            values.append(my_data.get(key, value))
     return tuple(values)
 
 
@@ -227,6 +229,7 @@ def train_model(
     gradient_accumulation_steps,
     mem_eff_attn,
     output_name,
+    model_list,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -480,7 +483,7 @@ def lora_tab(
     with gr.Tab('Source model'):
         # Define the input elements
         with gr.Row():
-            pretrained_model_name_or_path_input = gr.Textbox(
+            pretrained_model_name_or_path = gr.Textbox(
                 label='Pretrained model name or path',
                 placeholder='enter the path to custom model or name of pretrained model',
             )
@@ -489,15 +492,15 @@ def lora_tab(
             )
             pretrained_model_name_or_path_file.click(
                 get_any_file_path,
-                inputs=[pretrained_model_name_or_path_input],
-                outputs=pretrained_model_name_or_path_input,
+                inputs=[pretrained_model_name_or_path],
+                outputs=pretrained_model_name_or_path,
             )
             pretrained_model_name_or_path_folder = gr.Button(
                 folder_symbol, elem_id='open_folder_small'
             )
             pretrained_model_name_or_path_folder.click(
                 get_folder_path,
-                outputs=pretrained_model_name_or_path_input,
+                outputs=pretrained_model_name_or_path,
             )
             model_list = gr.Dropdown(
                 label='(Optional) Model Quick Pick',
@@ -524,67 +527,67 @@ def lora_tab(
             )
 
         with gr.Row():
-            v2_input = gr.Checkbox(label='v2', value=True)
-            v_parameterization_input = gr.Checkbox(
+            v2 = gr.Checkbox(label='v2', value=True)
+            v_parameterization = gr.Checkbox(
                 label='v_parameterization', value=False
             )
-        pretrained_model_name_or_path_input.change(
+        pretrained_model_name_or_path.change(
             remove_doublequote,
-            inputs=[pretrained_model_name_or_path_input],
-            outputs=[pretrained_model_name_or_path_input],
+            inputs=[pretrained_model_name_or_path],
+            outputs=[pretrained_model_name_or_path],
         )
         model_list.change(
             set_pretrained_model_name_or_path_input,
-            inputs=[model_list, v2_input, v_parameterization_input],
+            inputs=[model_list, v2, v_parameterization],
             outputs=[
-                pretrained_model_name_or_path_input,
-                v2_input,
-                v_parameterization_input,
+                pretrained_model_name_or_path,
+                v2,
+                v_parameterization,
             ],
         )
 
     with gr.Tab('Folders'):
         with gr.Row():
-            train_data_dir_input = gr.Textbox(
+            train_data_dir = gr.Textbox(
                 label='Image folder',
                 placeholder='Folder where the training folders containing the images are located',
             )
-            train_data_dir_input_folder = gr.Button(
+            train_data_dir_folder = gr.Button(
                 '📂', elem_id='open_folder_small'
             )
-            train_data_dir_input_folder.click(
-                get_folder_path, outputs=train_data_dir_input
+            train_data_dir_folder.click(
+                get_folder_path, outputs=train_data_dir
             )
-            reg_data_dir_input = gr.Textbox(
+            reg_data_dir = gr.Textbox(
                 label='Regularisation folder',
                 placeholder='(Optional) Folder where where the regularization folders containing the images are located',
             )
-            reg_data_dir_input_folder = gr.Button(
+            reg_data_dir_folder = gr.Button(
                 '📂', elem_id='open_folder_small'
             )
-            reg_data_dir_input_folder.click(
-                get_folder_path, outputs=reg_data_dir_input
+            reg_data_dir_folder.click(
+                get_folder_path, outputs=reg_data_dir
             )
         with gr.Row():
-            output_dir_input = gr.Textbox(
+            output_dir = gr.Textbox(
                 label='Output folder',
                 placeholder='Folder to output trained model',
             )
-            output_dir_input_folder = gr.Button(
+            output_dir_folder = gr.Button(
                 '📂', elem_id='open_folder_small'
             )
-            output_dir_input_folder.click(
-                get_folder_path, outputs=output_dir_input
+            output_dir_folder.click(
+                get_folder_path, outputs=output_dir
             )
-            logging_dir_input = gr.Textbox(
+            logging_dir = gr.Textbox(
                 label='Logging folder',
                 placeholder='Optional: enable logging and output TensorBoard log to this folder',
             )
-            logging_dir_input_folder = gr.Button(
+            logging_dir_folder = gr.Button(
                 '📂', elem_id='open_folder_small'
             )
-            logging_dir_input_folder.click(
-                get_folder_path, outputs=logging_dir_input
+            logging_dir_folder.click(
+                get_folder_path, outputs=logging_dir
             )
         with gr.Row():
             output_name = gr.Textbox(
@@ -593,25 +596,25 @@ def lora_tab(
                 value='last',
                 interactive=True,
             )
-        train_data_dir_input.change(
+        train_data_dir.change(
             remove_doublequote,
-            inputs=[train_data_dir_input],
-            outputs=[train_data_dir_input],
+            inputs=[train_data_dir],
+            outputs=[train_data_dir],
         )
-        reg_data_dir_input.change(
+        reg_data_dir.change(
             remove_doublequote,
-            inputs=[reg_data_dir_input],
-            outputs=[reg_data_dir_input],
+            inputs=[reg_data_dir],
+            outputs=[reg_data_dir],
         )
-        output_dir_input.change(
+        output_dir.change(
             remove_doublequote,
-            inputs=[output_dir_input],
-            outputs=[output_dir_input],
+            inputs=[output_dir],
+            outputs=[output_dir],
         )
-        logging_dir_input.change(
+        logging_dir.change(
             remove_doublequote,
-            inputs=[logging_dir_input],
-            outputs=[logging_dir_input],
+            inputs=[logging_dir],
+            outputs=[logging_dir],
         )
     with gr.Tab('Training parameters'):
         with gr.Row():
@@ -628,7 +631,7 @@ def lora_tab(
                 outputs=lora_network_weights,
             )
         with gr.Row():
-            lr_scheduler_input = gr.Dropdown(
+            lr_scheduler = gr.Dropdown(
                 label='LR Scheduler',
                 choices=[
                     'constant',
@@ -640,7 +643,7 @@ def lora_tab(
                 ],
                 value='cosine',
             )
-            lr_warmup_input = gr.Textbox(label='LR warmup (% of steps)', value=10)
+            lr_warmup = gr.Textbox(label='LR warmup (% of steps)', value=10)
         with gr.Row():
             text_encoder_lr = gr.Textbox(
                 label='Text Encoder learning rate',
@@ -659,19 +662,19 @@ def lora_tab(
                 interactive=True,
             )
         with gr.Row():
-            train_batch_size_input = gr.Slider(
+            train_batch_size = gr.Slider(
                 minimum=1,
                 maximum=32,
                 label='Train batch size',
                 value=1,
                 step=1,
             )
-            epoch_input = gr.Textbox(label='Epoch', value=1)
-            save_every_n_epochs_input = gr.Textbox(
+            epoch = gr.Textbox(label='Epoch', value=1)
+            save_every_n_epochs = gr.Textbox(
                 label='Save every N epochs', value=1
             )
         with gr.Row():
-            mixed_precision_input = gr.Dropdown(
+            mixed_precision = gr.Dropdown(
                 label='Mixed precision',
                 choices=[
                     'no',
@@ -680,7 +683,7 @@ def lora_tab(
                 ],
                 value='fp16',
             )
-            save_precision_input = gr.Dropdown(
+            save_precision = gr.Dropdown(
                 label='Save precision',
                 choices=[
                     'float',
@@ -689,7 +692,7 @@ def lora_tab(
                 ],
                 value='fp16',
             )
-            num_cpu_threads_per_process_input = gr.Slider(
+            num_cpu_threads_per_process = gr.Slider(
                 minimum=1,
                 maximum=os.cpu_count(),
                 step=1,
@@ -697,18 +700,18 @@ def lora_tab(
                 value=os.cpu_count(),
             )
         with gr.Row():
-            seed_input = gr.Textbox(label='Seed', value=1234)
-            max_resolution_input = gr.Textbox(
+            seed = gr.Textbox(label='Seed', value=1234)
+            max_resolution = gr.Textbox(
                 label='Max resolution',
                 value='512,512',
                 placeholder='512,512',
             )
         with gr.Row():
-            caption_extention_input = gr.Textbox(
+            caption_extention = gr.Textbox(
                 label='Caption Extension',
                 placeholder='(Optional) Extension for caption files. default: .caption',
             )
-            stop_text_encoder_training_input = gr.Slider(
+            stop_text_encoder_training = gr.Slider(
                 minimum=0,
                 maximum=100,
                 value=0,
@@ -716,20 +719,20 @@ def lora_tab(
                 label='Stop text encoder training',
             )
         with gr.Row():
-            enable_bucket_input = gr.Checkbox(
+            enable_bucket = gr.Checkbox(
                 label='Enable buckets', value=True
             )
-            cache_latent_input = gr.Checkbox(label='Cache latent', value=True)
-            use_8bit_adam_input = gr.Checkbox(
+            cache_latent = gr.Checkbox(label='Cache latent', value=True)
+            use_8bit_adam = gr.Checkbox(
                 label='Use 8bit adam', value=True
             )
-            xformers_input = gr.Checkbox(label='Use xformers', value=True)
+            xformers = gr.Checkbox(label='Use xformers', value=True)
         with gr.Accordion('Advanced Configuration', open=False):
             with gr.Row():
-                full_fp16_input = gr.Checkbox(
+                full_fp16 = gr.Checkbox(
                     label='Full fp16 training (experimental)', value=False
                 )
-                no_token_padding_input = gr.Checkbox(
+                no_token_padding = gr.Checkbox(
                     label='No token padding', value=False
                 )
 
@@ -754,7 +757,7 @@ def lora_tab(
                 color_aug.change(
                     color_aug_changed,
                     inputs=[color_aug],
-                    outputs=[cache_latent_input],
+                    outputs=[cache_latent],
                 )
                 clip_skip = gr.Slider(
                     label='Clip skip', value='1', minimum=1, maximum=12, step=1
@@ -783,10 +786,10 @@ def lora_tab(
             'This section provide Dreambooth tools to help setup your dataset...'
         )
         gradio_dreambooth_folder_creation_tab(
-            train_data_dir_input=train_data_dir_input,
-            reg_data_dir_input=reg_data_dir_input,
-            output_dir_input=output_dir_input,
-            logging_dir_input=logging_dir_input,
+            train_data_dir_input=train_data_dir,
+            reg_data_dir_input=reg_data_dir,
+            output_dir_input=output_dir,
+            logging_dir_input=logging_dir,
         )
         gradio_dataset_balancing_tab()
         gradio_merge_lora_tab()
@@ -794,32 +797,32 @@ def lora_tab(
     button_run = gr.Button('Train model')
 
     settings_list = [
-        pretrained_model_name_or_path_input,
-        v2_input,
-        v_parameterization_input,
-        logging_dir_input,
-        train_data_dir_input,
-        reg_data_dir_input,
-        output_dir_input,
-        max_resolution_input,
-        lr_scheduler_input,
-        lr_warmup_input,
-        train_batch_size_input,
-        epoch_input,
-        save_every_n_epochs_input,
-        mixed_precision_input,
-        save_precision_input,
-        seed_input,
-        num_cpu_threads_per_process_input,
-        cache_latent_input,
-        caption_extention_input,
-        enable_bucket_input,
+        pretrained_model_name_or_path,
+        v2,
+        v_parameterization,
+        logging_dir,
+        train_data_dir,
+        reg_data_dir,
+        output_dir,
+        max_resolution,
+        lr_scheduler,
+        lr_warmup,
+        train_batch_size,
+        epoch,
+        save_every_n_epochs,
+        mixed_precision,
+        save_precision,
+        seed,
+        num_cpu_threads_per_process,
+        cache_latent,
+        caption_extention,
+        enable_bucket,
         gradient_checkpointing,
-        full_fp16_input,
-        no_token_padding_input,
-        stop_text_encoder_training_input,
-        use_8bit_adam_input,
-        xformers_input,
+        full_fp16,
+        no_token_padding,
+        stop_text_encoder_training,
+        use_8bit_adam,
+        xformers,
         save_model_as_dropdown,
         shuffle_caption,
         save_state,
@@ -835,6 +838,7 @@ def lora_tab(
         gradient_accumulation_steps,
         mem_eff_attn,
         output_name,
+        model_list,
     ]
 
     button_open_config.click(
@@ -861,10 +865,10 @@ def lora_tab(
     )
 
     return (
-        train_data_dir_input,
-        reg_data_dir_input,
-        output_dir_input,
-        logging_dir_input,
+        train_data_dir,
+        reg_data_dir,
+        output_dir,
+        logging_dir,
     )