Move functions to common_gui

Add model name support

README.md

@@ -101,6 +101,9 @@ Once you have created the LoRA network you can generate images via auto1111 by i
 
 ## Change history
 
+* 2023/01/10 (v20.1):
+    - Add support for `--output_name` to trainers
+    - Refactor code for easier maintenance
 * 2023/01/10 (v20.0):
     - Update code base to match latest kohys_ss code upgrade in https://github.com/kohya-ss/sd-scripts
 * 2023/01/09 (v19.4.3):

dreambooth_gui.py

@@ -18,6 +18,8 @@ from library.common_gui import (
     get_any_file_path,
     get_saveasfile_path,
     color_aug_changed,
+    save_inference_file,
+    set_pretrained_model_name_or_path_input,
 )
 from library.dreambooth_folder_creation_gui import (
     gradio_dreambooth_folder_creation_tab,
@@ -102,45 +104,6 @@ def save_configuration(
             'save_as',
         ]
     }
-    # variables = {
-    #     'pretrained_model_name_or_path': pretrained_model_name_or_path,
-    #     'v2': v2,
-    #     'v_parameterization': v_parameterization,
-    #     'logging_dir': logging_dir,
-    #     'train_data_dir': train_data_dir,
-    #     'reg_data_dir': reg_data_dir,
-    #     'output_dir': output_dir,
-    #     'max_resolution': max_resolution,
-    #     'learning_rate': learning_rate,
-    #     'lr_scheduler': lr_scheduler,
-    #     'lr_warmup': lr_warmup,
-    #     '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,
-    #     'cache_latent': cache_latent,
-    #     'caption_extention': caption_extention,
-    #     'enable_bucket': enable_bucket,
-    #     'gradient_checkpointing': gradient_checkpointing,
-    #     'full_fp16': full_fp16,
-    #     'no_token_padding': no_token_padding,
-    #     'stop_text_encoder_training': stop_text_encoder_training,
-    #     'use_8bit_adam': use_8bit_adam,
-    #     'xformers': xformers,
-    #     'save_model_as': save_model_as,
-    #     'shuffle_caption': shuffle_caption,
-    #     'save_state': save_state,
-    #     'resume': resume,
-    #     'prior_loss_weight': prior_loss_weight,
-    #     'color_aug': color_aug,
-    #     'flip_aug': flip_aug,
-    #     'clip_skip': clip_skip,
-    #     'vae': vae,
-    #     'output_name': output_name,
-    # }
 
     # Save the data to the selected file
     with open(file_path, 'w') as file:
@@ -194,71 +157,24 @@ def open_configuration(
 
     original_file_path = file_path
     file_path = get_file_path(file_path)
-    # print(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 = json.load(f)
+            my_data_db = 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 = {}
+        my_data_db = {}
 
     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.get(key, value))
+            values.append(my_data_db.get(key, value))
-    # print(values)
     return tuple(values)
 
-    # 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('logging_dir', logging_dir),
-    #     my_data.get('train_data_dir', train_data_dir),
-    #     my_data.get('reg_data_dir', reg_data_dir),
-    #     my_data.get('output_dir', output_dir),
-    #     my_data.get('max_resolution', max_resolution),
-    #     my_data.get('learning_rate', learning_rate),
-    #     my_data.get('lr_scheduler', lr_scheduler),
-    #     my_data.get('lr_warmup', lr_warmup),
-    #     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('cache_latent', cache_latent),
-    #     my_data.get('caption_extention', caption_extention),
-    #     my_data.get('enable_bucket', enable_bucket),
-    #     my_data.get('gradient_checkpointing', gradient_checkpointing),
-    #     my_data.get('full_fp16', full_fp16),
-    #     my_data.get('no_token_padding', no_token_padding),
-    #     my_data.get('stop_text_encoder_training', stop_text_encoder_training),
-    #     my_data.get('use_8bit_adam', use_8bit_adam),
-    #     my_data.get('xformers', xformers),
-    #     my_data.get('save_model_as', save_model_as),
-    #     my_data.get('shuffle_caption', shuffle_caption),
-    #     my_data.get('save_state', save_state),
-    #     my_data.get('resume', resume),
-    #     my_data.get('prior_loss_weight', prior_loss_weight),
-    #     my_data.get('color_aug', color_aug),
-    #     my_data.get('flip_aug', flip_aug),
-    #     my_data.get('clip_skip', clip_skip),
-    #     my_data.get('vae', vae),
-    #     my_data.get('output_name', output_name),
-    # )
-
-
 def train_model(
     pretrained_model_name_or_path,
     v2,
@@ -298,29 +214,6 @@ def train_model(
     vae,
     output_name,
 ):
-    def save_inference_file(output_dir, v2, v_parameterization, output_name):
-        # List all files in the directory
-        files = os.listdir(output_dir)
-
-        # Iterate over the list of files
-        for file in files:
-            # Check if the file starts with the value of save_inference_file
-            if file.startswith(output_name):
-                # Copy the v2-inference-v.yaml file to the current file, with a .yaml extension
-                if v2 and v_parameterization:
-                    print(f'Saving v2-inference-v.yaml as {output_dir}/{file}.yaml')
-                    shutil.copy(
-                        f'./v2_inference/v2-inference-v.yaml',
-                        f'{output_dir}/{file}.yaml',
-                    )
-                elif v2:
-                    print(f'Saving v2-inference.yaml as {output_dir}/{file}.yaml')
-                    shutil.copy(
-                        f'./v2_inference/v2-inference.yaml',
-                        f'{output_dir}/{file}.yaml',
-                    )
-
-
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
         return
@@ -487,57 +380,6 @@ def train_model(
         save_inference_file(output_dir, v2, v_parameterization, output_name)
 
 
-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 UI(username, password):
     css = ''
 
@@ -593,11 +435,6 @@ def dreambooth_tab(
                 placeholder="type the configuration file path or use the 'Open' button above to select it...",
                 interactive=True,
             )
-        # 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():

fine_tune.py

@@ -16,321 +16,456 @@ import library.train_util as train_util
 
 
 def collate_fn(examples):
-  return examples[0]
+    return examples[0]
 
 
 def train(args):
-  train_util.verify_training_args(args)
+    train_util.verify_training_args(args)
-  train_util.prepare_dataset_args(args, True)
+    train_util.prepare_dataset_args(args, True)
 
-  cache_latents = args.cache_latents
+    cache_latents = args.cache_latents
 
-  if args.seed is not None:
+    if args.seed is not None:
-    set_seed(args.seed)                           # 乱数系列を初期化する
+        set_seed(args.seed)                           # 乱数系列を初期化する
 
-  tokenizer = train_util.load_tokenizer(args)
+    tokenizer = train_util.load_tokenizer(args)
 
-  train_dataset = train_util.FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
+    train_dataset = train_util.FineTuningDataset(
-                                               tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
+        args.in_json,
-                                               args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
+        args.train_batch_size,
-                                               args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
+        args.train_data_dir,
-                                               args.dataset_repeats, args.debug_dataset)
+        tokenizer,
-  train_dataset.make_buckets()
+        args.max_token_length,
+        args.shuffle_caption,
+        args.keep_tokens,
+        args.resolution,
+        args.enable_bucket,
+        args.min_bucket_reso,
+        args.max_bucket_reso,
+        args.flip_aug,
+        args.color_aug,
+        args.face_crop_aug_range,
+        args.random_crop,
+        args.dataset_repeats,
+        args.debug_dataset,
+    )
+    train_dataset.make_buckets()
 
-  if args.debug_dataset:
+    if args.debug_dataset:
-    train_util.debug_dataset(train_dataset)
+        train_util.debug_dataset(train_dataset)
-    return
+        return
-  if len(train_dataset) == 0:
+    if len(train_dataset) == 0:
-    print("No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。")
+        print(
-    return
+            'No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。'
+        )
+        return
 
-  # acceleratorを準備する
+    # acceleratorを準備する
-  print("prepare accelerator")
+    print('prepare accelerator')
-  accelerator, unwrap_model = train_util.prepare_accelerator(args)
+    accelerator, unwrap_model = train_util.prepare_accelerator(args)
 
-  # mixed precisionに対応した型を用意しておき適宜castする
+    # mixed precisionに対応した型を用意しておき適宜castする
-  weight_dtype, save_dtype = train_util.prepare_dtype(args)
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
 
-  # モデルを読み込む
+    # モデルを読み込む
-  text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
+    (
+        text_encoder,
+        vae,
+        unet,
+        load_stable_diffusion_format,
+    ) = train_util.load_target_model(args, weight_dtype)
 
-  # verify load/save model formats
+    # verify load/save model formats
-  if load_stable_diffusion_format:
+    if load_stable_diffusion_format:
-    src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
+        src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
-    src_diffusers_model_path = None
+        src_diffusers_model_path = None
-  else:
-    src_stable_diffusion_ckpt = None
-    src_diffusers_model_path = args.pretrained_model_name_or_path
-
-  if args.save_model_as is None:
-    save_stable_diffusion_format = load_stable_diffusion_format
-    use_safetensors = args.use_safetensors
-  else:
-    save_stable_diffusion_format = args.save_model_as.lower() == 'ckpt' or args.save_model_as.lower() == 'safetensors'
-    use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
-
-  # Diffusers版のxformers使用フラグを設定する関数
-  def set_diffusers_xformers_flag(model, valid):
-    #   model.set_use_memory_efficient_attention_xformers(valid)            # 次のリリースでなくなりそう
-    # pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
-    # U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
-    # 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
-
-    # Recursively walk through all the children.
-    # Any children which exposes the set_use_memory_efficient_attention_xformers method
-    # gets the message
-    def fn_recursive_set_mem_eff(module: torch.nn.Module):
-      if hasattr(module, "set_use_memory_efficient_attention_xformers"):
-        module.set_use_memory_efficient_attention_xformers(valid)
-
-      for child in module.children():
-        fn_recursive_set_mem_eff(child)
-
-    fn_recursive_set_mem_eff(model)
-
-  # モデルに xformers とか memory efficient attention を組み込む
-  if args.diffusers_xformers:
-    print("Use xformers by Diffusers")
-    set_diffusers_xformers_flag(unet, True)
-  else:
-    # Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
-    print("Disable Diffusers' xformers")
-    set_diffusers_xformers_flag(unet, False)
-    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
-
-  # 学習を準備する
-  if cache_latents:
-    vae.to(accelerator.device, dtype=weight_dtype)
-    vae.requires_grad_(False)
-    vae.eval()
-    with torch.no_grad():
-      train_dataset.cache_latents(vae)
-    vae.to("cpu")
-    if torch.cuda.is_available():
-      torch.cuda.empty_cache()
-    gc.collect()
-
-  # 学習を準備する：モデルを適切な状態にする
-  training_models = []
-  if args.gradient_checkpointing:
-    unet.enable_gradient_checkpointing()
-  training_models.append(unet)
-
-  if args.train_text_encoder:
-    print("enable text encoder training")
-    if args.gradient_checkpointing:
-      text_encoder.gradient_checkpointing_enable()
-    training_models.append(text_encoder)
-  else:
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-    text_encoder.requires_grad_(False)             # text encoderは学習しない
-    if args.gradient_checkpointing:
-      text_encoder.gradient_checkpointing_enable()
-      text_encoder.train()                # required for gradient_checkpointing
     else:
-      text_encoder.eval()
+        src_stable_diffusion_ckpt = None
+        src_diffusers_model_path = args.pretrained_model_name_or_path
 
-  if not cache_latents:
+    if args.save_model_as is None:
-    vae.requires_grad_(False)
+        save_stable_diffusion_format = load_stable_diffusion_format
-    vae.eval()
+        use_safetensors = args.use_safetensors
-    vae.to(accelerator.device, dtype=weight_dtype)
+    else:
+        save_stable_diffusion_format = (
+            args.save_model_as.lower() == 'ckpt'
+            or args.save_model_as.lower() == 'safetensors'
+        )
+        use_safetensors = args.use_safetensors or (
+            'safetensors' in args.save_model_as.lower()
+        )
 
-  for m in training_models:
+    # Diffusers版のxformers使用フラグを設定する関数
-    m.requires_grad_(True)
+    def set_diffusers_xformers_flag(model, valid):
-  params = []
+        #   model.set_use_memory_efficient_attention_xformers(valid)            # 次のリリースでなくなりそう
-  for m in training_models:
+        # pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
-    params.extend(m.parameters())
+        # U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
-  params_to_optimize = params
+        # 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
 
-  # 学習に必要なクラスを準備する
+        # Recursively walk through all the children.
-  print("prepare optimizer, data loader etc.")
+        # Any children which exposes the set_use_memory_efficient_attention_xformers method
+        # gets the message
+        def fn_recursive_set_mem_eff(module: torch.nn.Module):
+            if hasattr(module, 'set_use_memory_efficient_attention_xformers'):
+                module.set_use_memory_efficient_attention_xformers(valid)
 
-  # 8-bit Adamを使う
+            for child in module.children():
-  if args.use_8bit_adam:
+                fn_recursive_set_mem_eff(child)
-    try:
-      import bitsandbytes as bnb
-    except ImportError:
-      raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
-    print("use 8-bit Adam optimizer")
-    optimizer_class = bnb.optim.AdamW8bit
-  else:
-    optimizer_class = torch.optim.AdamW
 
-  # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
+        fn_recursive_set_mem_eff(model)
-  optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
 
-  # dataloaderを準備する
+    # モデルに xformers とか memory efficient attention を組み込む
-  # DataLoaderのプロセス数：0はメインプロセスになる
+    if args.diffusers_xformers:
-  n_workers = min(8, os.cpu_count() - 1)      # cpu_count-1 ただし最大8
+        print('Use xformers by Diffusers')
-  train_dataloader = torch.utils.data.DataLoader(
+        set_diffusers_xformers_flag(unet, True)
-      train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
+    else:
+        # Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
+        print("Disable Diffusers' xformers")
+        set_diffusers_xformers_flag(unet, False)
+        train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
 
-  # lr schedulerを用意する
+    # 学習を準備する
-  lr_scheduler = diffusers.optimization.get_scheduler(
+    if cache_latents:
-      args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
+        vae.to(accelerator.device, dtype=weight_dtype)
-
+        vae.requires_grad_(False)
-  # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
+        vae.eval()
-  if args.full_fp16:
-    assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
-    print("enable full fp16 training.")
-    unet.to(weight_dtype)
-    text_encoder.to(weight_dtype)
-
-  # acceleratorがなんかよろしくやってくれるらしい
-  if args.train_text_encoder:
-    unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, text_encoder, optimizer, train_dataloader, lr_scheduler)
-  else:
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
-
-  # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
-  if args.full_fp16:
-    train_util.patch_accelerator_for_fp16_training(accelerator)
-
-  # resumeする
-  if args.resume is not None:
-    print(f"resume training from state: {args.resume}")
-    accelerator.load_state(args.resume)
-
-  # epoch数を計算する
-  num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-  num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-  # 学習する
-  total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-  print("running training / 学習開始")
-  print(f"  num examples / サンプル数: {train_dataset.num_train_images}")
-  print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
-  print(f"  num epochs / epoch数: {num_train_epochs}")
-  print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
-  print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
-  print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
-  print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
-
-  progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
-  global_step = 0
-
-  noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
-                                  num_train_timesteps=1000, clip_sample=False)
-
-  if accelerator.is_main_process:
-    accelerator.init_trackers("finetuning")
-
-  for epoch in range(num_train_epochs):
-    print(f"epoch {epoch+1}/{num_train_epochs}")
-    for m in training_models:
-      m.train()
-
-    loss_total = 0
-    for step, batch in enumerate(train_dataloader):
-      with accelerator.accumulate(training_models[0]):  # 複数モデルに対応していない模様だがとりあえずこうしておく
         with torch.no_grad():
-          if "latents" in batch and batch["latents"] is not None:
+            train_dataset.cache_latents(vae)
-            latents = batch["latents"].to(accelerator.device)
+        vae.to('cpu')
-          else:
+        if torch.cuda.is_available():
-            # latentに変換
+            torch.cuda.empty_cache()
-            latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+        gc.collect()
-          latents = latents * 0.18215
-        b_size = latents.shape[0]
 
-        with torch.set_grad_enabled(args.train_text_encoder):
+    # 学習を準備する：モデルを適切な状態にする
-          # Get the text embedding for conditioning
+    training_models = []
-          input_ids = batch["input_ids"].to(accelerator.device)
+    if args.gradient_checkpointing:
-          encoder_hidden_states = train_util.get_hidden_states(
+        unet.enable_gradient_checkpointing()
-              args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype)
+    training_models.append(unet)
 
-        # Sample noise that we'll add to the latents
+    if args.train_text_encoder:
-        noise = torch.randn_like(latents, device=latents.device)
+        print('enable text encoder training')
-
+        if args.gradient_checkpointing:
-        # Sample a random timestep for each image
+            text_encoder.gradient_checkpointing_enable()
-        timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+        training_models.append(text_encoder)
-        timesteps = timesteps.long()
+    else:
-
+        text_encoder.to(accelerator.device, dtype=weight_dtype)
-        # Add noise to the latents according to the noise magnitude at each timestep
+        text_encoder.requires_grad_(False)             # text encoderは学習しない
-        # (this is the forward diffusion process)
+        if args.gradient_checkpointing:
-        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+            text_encoder.gradient_checkpointing_enable()
-
+            text_encoder.train()                # required for gradient_checkpointing
-        # Predict the noise residual
-        noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-        if args.v_parameterization:
-          # v-parameterization training
-          target = noise_scheduler.get_velocity(latents, noise, timesteps)
         else:
-          target = noise
+            text_encoder.eval()
 
-        loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
 
-        accelerator.backward(loss)
+    for m in training_models:
-        if accelerator.sync_gradients:
+        m.requires_grad_(True)
-          params_to_clip = []
+    params = []
-          for m in training_models:
+    for m in training_models:
-            params_to_clip.extend(m.parameters())
+        params.extend(m.parameters())
-          accelerator.clip_grad_norm_(params_to_clip, 1.0)  # args.max_grad_norm)
+    params_to_optimize = params
 
-        optimizer.step()
+    # 学習に必要なクラスを準備する
-        lr_scheduler.step()
+    print('prepare optimizer, data loader etc.')
-        optimizer.zero_grad(set_to_none=True)
 
-      # Checks if the accelerator has performed an optimization step behind the scenes
+    # 8-bit Adamを使う
-      if accelerator.sync_gradients:
+    if args.use_8bit_adam:
-        progress_bar.update(1)
+        try:
-        global_step += 1
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                'No bitsand bytes / bitsandbytesがインストールされていないようです'
+            )
+        print('use 8-bit Adam optimizer')
+        optimizer_class = bnb.optim.AdamW8bit
+    else:
+        optimizer_class = torch.optim.AdamW
 
-      current_loss = loss.detach().item()        # 平均なのでbatch sizeは関係ないはず
+    # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
-      if args.logging_dir is not None:
+    optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
-        logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
-        accelerator.log(logs, step=global_step)
 
-      loss_total += current_loss
+    # dataloaderを準備する
-      avr_loss = loss_total / (step+1)
+    # DataLoaderのプロセス数：0はメインプロセスになる
-      logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+    n_workers = min(8, os.cpu_count() - 1)      # cpu_count-1 ただし最大8
-      progress_bar.set_postfix(**logs)
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=1,
+        shuffle=False,
+        collate_fn=collate_fn,
+        num_workers=n_workers,
+    )
 
-      if global_step >= args.max_train_steps:
+    # lr schedulerを用意する
-        break
+    lr_scheduler = diffusers.optimization.get_scheduler(
+        args.lr_scheduler,
+        optimizer,
+        num_warmup_steps=args.lr_warmup_steps,
+        num_training_steps=args.max_train_steps
+        * args.gradient_accumulation_steps,
+    )
 
-    if args.logging_dir is not None:
+    # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+    if args.full_fp16:
-      accelerator.log(logs, step=epoch+1)
+        assert (
+            args.mixed_precision == 'fp16'
+        ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
+        print('enable full fp16 training.')
+        unet.to(weight_dtype)
+        text_encoder.to(weight_dtype)
 
-    accelerator.wait_for_everyone()
+    # acceleratorがなんかよろしくやってくれるらしい
+    if args.train_text_encoder:
+        (
+            unet,
+            text_encoder,
+            optimizer,
+            train_dataloader,
+            lr_scheduler,
+        ) = accelerator.prepare(
+            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
+        )
+    else:
+        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            unet, optimizer, train_dataloader, lr_scheduler
+        )
 
-    if args.save_every_n_epochs is not None:
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
-      src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
+    if args.full_fp16:
-      train_util.save_sd_model_on_epoch_end(args, accelerator, src_path, save_stable_diffusion_format, use_safetensors,
+        train_util.patch_accelerator_for_fp16_training(accelerator)
-                                            save_dtype, epoch, num_train_epochs, global_step,  unwrap_model(text_encoder), unwrap_model(unet), vae)
 
-  is_main_process = accelerator.is_main_process
+    # resumeする
-  if is_main_process:
+    if args.resume is not None:
-    unet = unwrap_model(unet)
+        print(f'resume training from state: {args.resume}')
-    text_encoder = unwrap_model(text_encoder)
+        accelerator.load_state(args.resume)
 
-  accelerator.end_training()
+    # epoch数を計算する
+    num_update_steps_per_epoch = math.ceil(
+        len(train_dataloader) / args.gradient_accumulation_steps
+    )
+    num_train_epochs = math.ceil(
+        args.max_train_steps / num_update_steps_per_epoch
+    )
 
-  if args.save_state:
+    # 学習する
-    train_util.save_state_on_train_end(args, accelerator)
+    total_batch_size = (
+        args.train_batch_size
+        * accelerator.num_processes
+        * args.gradient_accumulation_steps
+    )
+    print('running training / 学習開始')
+    print(f'  num examples / サンプル数: {train_dataset.num_train_images}')
+    print(f'  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}')
+    print(f'  num epochs / epoch数: {num_train_epochs}')
+    print(f'  batch size per device / バッチサイズ: {args.train_batch_size}')
+    print(
+        f'  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}'
+    )
+    print(
+        f'  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}'
+    )
+    print(f'  total optimization steps / 学習ステップ数: {args.max_train_steps}')
 
-  del accelerator                         # この後メモリを使うのでこれは消す
+    progress_bar = tqdm(
+        range(args.max_train_steps),
+        smoothing=0,
+        disable=not accelerator.is_local_main_process,
+        desc='steps',
+    )
+    global_step = 0
 
-  if is_main_process:
+    noise_scheduler = DDPMScheduler(
-    src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
+        beta_start=0.00085,
-    train_util.save_sd_model_on_train_end(args, src_path, save_stable_diffusion_format, use_safetensors,
+        beta_end=0.012,
-                                          save_dtype, epoch, global_step,  text_encoder, unet, vae)
+        beta_schedule='scaled_linear',
-    print("model saved.")
+        num_train_timesteps=1000,
+        clip_sample=False,
+    )
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers('finetuning')
+
+    for epoch in range(num_train_epochs):
+        print(f'epoch {epoch+1}/{num_train_epochs}')
+        for m in training_models:
+            m.train()
+
+        loss_total = 0
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(
+                training_models[0]
+            ):  # 複数モデルに対応していない模様だがとりあえずこうしておく
+                with torch.no_grad():
+                    if 'latents' in batch and batch['latents'] is not None:
+                        latents = batch['latents'].to(accelerator.device)
+                    else:
+                        # latentに変換
+                        latents = vae.encode(
+                            batch['images'].to(dtype=weight_dtype)
+                        ).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                with torch.set_grad_enabled(args.train_text_encoder):
+                    # Get the text embedding for conditioning
+                    input_ids = batch['input_ids'].to(accelerator.device)
+                    encoder_hidden_states = train_util.get_hidden_states(
+                        args,
+                        input_ids,
+                        tokenizer,
+                        text_encoder,
+                        None if not args.full_fp16 else weight_dtype,
+                    )
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+
+                # Sample a random timestep for each image
+                timesteps = torch.randint(
+                    0,
+                    noise_scheduler.config.num_train_timesteps,
+                    (b_size,),
+                    device=latents.device,
+                )
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(
+                    latents, noise, timesteps
+                )
+
+                # Predict the noise residual
+                noise_pred = unet(
+                    noisy_latents, timesteps, encoder_hidden_states
+                ).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(
+                        latents, noise, timesteps
+                    )
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(
+                    noise_pred.float(), target.float(), reduction='mean'
+                )
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    params_to_clip = []
+                    for m in training_models:
+                        params_to_clip.extend(m.parameters())
+                    accelerator.clip_grad_norm_(
+                        params_to_clip, 1.0
+                    )  # args.max_grad_norm)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            current_loss = loss.detach().item()        # 平均なのでbatch sizeは関係ないはず
+            if args.logging_dir is not None:
+                logs = {
+                    'loss': current_loss,
+                    'lr': lr_scheduler.get_last_lr()[0],
+                }
+                accelerator.log(logs, step=global_step)
+
+            loss_total += current_loss
+            avr_loss = loss_total / (step + 1)
+            logs = {'loss': avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.logging_dir is not None:
+            logs = {'epoch_loss': loss_total / len(train_dataloader)}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        if args.save_every_n_epochs is not None:
+            src_path = (
+                src_stable_diffusion_ckpt
+                if save_stable_diffusion_format
+                else src_diffusers_model_path
+            )
+            train_util.save_sd_model_on_epoch_end(
+                args,
+                accelerator,
+                src_path,
+                save_stable_diffusion_format,
+                use_safetensors,
+                save_dtype,
+                epoch,
+                num_train_epochs,
+                global_step,
+                unwrap_model(text_encoder),
+                unwrap_model(unet),
+                vae,
+            )
+
+    is_main_process = accelerator.is_main_process
+    if is_main_process:
+        unet = unwrap_model(unet)
+        text_encoder = unwrap_model(text_encoder)
+
+    accelerator.end_training()
+
+    if args.save_state:
+        train_util.save_state_on_train_end(args, accelerator)
+
+    del accelerator                         # この後メモリを使うのでこれは消す
+
+    if is_main_process:
+        src_path = (
+            src_stable_diffusion_ckpt
+            if save_stable_diffusion_format
+            else src_diffusers_model_path
+        )
+        train_util.save_sd_model_on_train_end(
+            args,
+            src_path,
+            save_stable_diffusion_format,
+            use_safetensors,
+            save_dtype,
+            epoch,
+            global_step,
+            text_encoder,
+            unet,
+            vae,
+        )
+        print('model saved.')
 
 
 if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser()
 
-  train_util.add_sd_models_arguments(parser)
+    train_util.add_sd_models_arguments(parser)
-  train_util.add_dataset_arguments(parser, False, True)
+    train_util.add_dataset_arguments(parser, False, True)
-  train_util.add_training_arguments(parser, False)
+    train_util.add_training_arguments(parser, False)
-  train_util.add_sd_saving_arguments(parser)
+    train_util.add_sd_saving_arguments(parser)
 
-  parser.add_argument("--diffusers_xformers", action='store_true',
+    parser.add_argument(
-                      help='use xformers by diffusers / Diffusersでxformersを使用する')
+        '--diffusers_xformers',
-  parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
+        action='store_true',
+        help='use xformers by diffusers / Diffusersでxformersを使用する',
+    )
+    parser.add_argument(
+        '--train_text_encoder',
+        action='store_true',
+        help='train text encoder / text encoderも学習する',
+    )
 
-  args = parser.parse_args()
+    args = parser.parse_args()
-  train(args)
+    train(args)

finetune_gui.py

@@ -11,6 +11,8 @@ from library.common_gui import (
     get_file_path,
     get_any_file_path,
     get_saveasfile_path,
+    save_inference_file,
+    set_pretrained_model_name_or_path_input,
 )
 from library.utilities import utilities_tab
 
@@ -63,7 +65,11 @@ def save_configuration(
     gradient_accumulation_steps,
     mem_eff_attn,
     shuffle_caption,
+    output_name,
 ):
+    # Get list of function parameters and values
+    parameters = list(locals().items())
+    
     original_file_path = file_path
 
     save_as_bool = True if save_as.get('label') == 'True' else False
@@ -83,51 +89,18 @@ def save_configuration(
 
     # Return the values of the variables as a dictionary
     variables = {
-        'pretrained_model_name_or_path': pretrained_model_name_or_path,
+        name: value
-        'v2': v2,
+        for name, value in parameters  # locals().items()
-        'v_parameterization': v_parameterization,
+        if name
-        'train_dir': train_dir,
+        not in [
-        'image_folder': image_folder,
+            'file_path',
-        'output_dir': output_dir,
+            'save_as',
-        '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_state': save_state,
-        'resume': resume,
-        'gradient_checkpointing': gradient_checkpointing,
-        'gradient_accumulation_steps': gradient_accumulation_steps,
-        'mem_eff_attn': mem_eff_attn,
-        'shuffle_caption': shuffle_caption,
     }
 
     # Save the data to the selected file
     with open(file_path, 'w') as file:
-        json.dump(variables, file)
+        json.dump(variables, file, indent=2)
 
     return file_path
 
@@ -174,7 +147,11 @@ def open_config_file(
     gradient_accumulation_steps,
     mem_eff_attn,
     shuffle_caption,
+    output_name,
 ):
+    # Get list of function parameters and values
+    parameters = list(locals().items())
+    
     original_file_path = file_path
     file_path = get_file_path(file_path)
 
@@ -182,59 +159,18 @@ def open_config_file(
         print(f'Loading config file {file_path}')
         # load variables from JSON file
         with open(file_path, 'r') as f:
-            my_data = json.load(f)
+            my_data_ft = 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 = {}
+        my_data_ft = {}
-
+    
-    # Return the values of the variables as a dictionary
+    values = [file_path]
-    return (
+    for key, value in parameters:
-        file_path,
+        # Set the value in the dictionary to the corresponding value in `my_data_ft`, or the default value if not found
-        my_data.get(
+        if not key in ['file_path']:
-            'pretrained_model_name_or_path', pretrained_model_name_or_path
+            values.append(my_data_ft.get(key, value))
-        ),
+    # print(values)
-        my_data.get('v2', v2),
+    return tuple(values)
-        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),
-        my_data.get('save_state', save_state),
-        my_data.get('resume', resume),
-        my_data.get('gradient_checkpointing', gradient_checkpointing),
-        my_data.get('gradient_accumulation_steps', gradient_accumulation_steps),
-        my_data.get('mem_eff_attn', mem_eff_attn),
-        my_data.get('shuffle_caption', shuffle_caption),
-    )
 
 
 def train_model(
@@ -278,22 +214,8 @@ def train_model(
     gradient_accumulation_steps,
     mem_eff_attn,
     shuffle_caption,
+    output_name,
 ):
-    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):
@@ -407,68 +329,19 @@ def train_model(
         run_cmd += ' --save_state'
     if not resume == '':
         run_cmd += f' --resume={resume}'
+    if not output_name == '':
+        run_cmd += f' --output_name="{output_name}"'
 
     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')
+    last_dir = pathlib.Path(f'{output_dir}/{output_name}')
 
     if not last_dir.is_dir():
         # Copy inference model for v2 if required
-        save_inference_file(output_dir, v2, v_parameterization)
+        save_inference_file(output_dir, v2, v_parameterization, output_name)
-
-
-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):
@@ -610,7 +483,7 @@ def finetune_tab():
             )
         with gr.Row():
             output_dir_input = gr.Textbox(
-                label='Output folder',
+                label='Model output folder',
                 placeholder='folder where the model will be saved',
             )
             output_dir_input_folder = gr.Button(
@@ -630,6 +503,13 @@ def finetune_tab():
             logging_dir_input_folder.click(
                 get_folder_path, outputs=logging_dir_input
             )
+        with gr.Row():
+            output_name = gr.Textbox(
+                label='Model output name',
+                placeholder='Name of the model to output',
+                value='last',
+                interactive=True,
+            )
         train_dir_input.change(
             remove_doublequote,
             inputs=[train_dir_input],
@@ -814,6 +694,7 @@ def finetune_tab():
         gradient_accumulation_steps,
         mem_eff_attn,
         shuffle_caption,
+        output_name,
     ]
 
     button_run.click(train_model, inputs=settings_list)

library/common_gui.py

@@ -2,6 +2,7 @@ from tkinter import filedialog, Tk
 import os
 import gradio as gr
 from easygui import msgbox
+import shutil
 
 def get_dir_and_file(file_path):
     dir_path, file_name = os.path.split(file_path)
@@ -183,4 +184,81 @@ def color_aug_changed(color_aug):
         msgbox('Disabling "Cache latent" because "Color augmentation" has been selected...')
         return gr.Checkbox.update(value=False, interactive=False)
     else:
-        return gr.Checkbox.update(value=True, interactive=True)
+        return gr.Checkbox.update(value=True, interactive=True)
+    
+def save_inference_file(output_dir, v2, v_parameterization, output_name):
+    # List all files in the directory
+    files = os.listdir(output_dir)
+
+    # Iterate over the list of files
+    for file in files:
+        # Check if the file starts with the value of output_name
+        if file.startswith(output_name):
+            # Check if it is a file or a directory
+            if os.path.isfile(os.path.join(output_dir, file)):
+                # Split the file name and extension
+                file_name, ext = os.path.splitext(file)
+                
+                # Copy the v2-inference-v.yaml file to the current file, with a .yaml extension
+                if v2 and v_parameterization:
+                    print(f'Saving v2-inference-v.yaml as {output_dir}/{file_name}.yaml')
+                    shutil.copy(
+                        f'./v2_inference/v2-inference-v.yaml',
+                        f'{output_dir}/{file_name}.yaml',
+                    )
+                elif v2:
+                    print(f'Saving v2-inference.yaml as {output_dir}/{file_name}.yaml')
+                    shutil.copy(
+                        f'./v2_inference/v2-inference.yaml',
+                        f'{output_dir}/{file_name}.yaml',
+                    )
+
+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

lora_gui.py

@@ -18,6 +18,8 @@ from library.common_gui import (
     get_any_file_path,
     get_saveasfile_path,
     color_aug_changed,
+    save_inference_file,
+    set_pretrained_model_name_or_path_input,
 )
 from library.dreambooth_folder_creation_gui import (
     gradio_dreambooth_folder_creation_tab,
@@ -76,8 +78,11 @@ def save_configuration(
     clip_skip,
     gradient_accumulation_steps,
     mem_eff_attn,
-    # vae,
+    output_name,
 ):
+    # Get list of function parameters and values
+    parameters = list(locals().items())
+
     original_file_path = file_path
 
     save_as_bool = True if save_as.get('label') == 'True' else False
@@ -97,85 +102,51 @@ def save_configuration(
 
     # Return the values of the variables as a dictionary
     variables = {
-        'pretrained_model_name_or_path': pretrained_model_name_or_path,
+        name: value
-        'v2': v2,
+        for name, value in parameters  # locals().items()
-        'v_parameterization': v_parameterization,
+        if name
-        'logging_dir': logging_dir,
+        not in [
-        'train_data_dir': train_data_dir,
+            'file_path',
-        'reg_data_dir': reg_data_dir,
+            'save_as',
-        'output_dir': output_dir,
+        ]
-        'max_resolution': max_resolution,
-        'lr_scheduler': lr_scheduler,
-        'lr_warmup': lr_warmup,
-        '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,
-        'cache_latent': cache_latent,
-        'caption_extention': caption_extention,
-        'enable_bucket': enable_bucket,
-        'gradient_checkpointing': gradient_checkpointing,
-        'full_fp16': full_fp16,
-        'no_token_padding': no_token_padding,
-        'stop_text_encoder_training': stop_text_encoder_training,
-        'use_8bit_adam': use_8bit_adam,
-        'xformers': xformers,
-        'save_model_as': save_model_as,
-        'shuffle_caption': shuffle_caption,
-        'save_state': save_state,
-        'resume': resume,
-        'prior_loss_weight': prior_loss_weight,
-        'text_encoder_lr': text_encoder_lr,
-        'unet_lr': unet_lr,
-        'network_dim': network_dim,
-        'lora_network_weights': lora_network_weights,
-        'color_aug': color_aug,
-        'flip_aug': flip_aug,
-        'clip_skip': clip_skip,
-        'gradient_accumulation_steps': gradient_accumulation_steps,
-        'mem_eff_attn': mem_eff_attn,
-        # 'vae': vae,
     }
 
     # Save the data to the selected file
     with open(file_path, 'w') as file:
-        json.dump(variables, file)
+        json.dump(variables, file, indent=2)
 
     return file_path
 
 
 def open_configuration(
     file_path,
-    pretrained_model_name_or_path,
+    pretrained_model_name_or_path_input,
-    v2,
+    v2_input,
-    v_parameterization,
+    v_parameterization_input,
-    logging_dir,
+    logging_dir_input,
-    train_data_dir,
+    train_data_dir_input,
-    reg_data_dir,
+    reg_data_dir_input,
-    output_dir,
+    output_dir_input,
-    max_resolution,
+    max_resolution_input,
-    lr_scheduler,
+    lr_scheduler_input,
-    lr_warmup,
+    lr_warmup_input,
-    train_batch_size,
+    train_batch_size_input,
-    epoch,
+    epoch_input,
-    save_every_n_epochs,
+    save_every_n_epochs_input,
-    mixed_precision,
+    mixed_precision_input,
-    save_precision,
+    save_precision_input,
-    seed,
+    seed_input,
-    num_cpu_threads_per_process,
+    num_cpu_threads_per_process_input,
-    cache_latent,
+    cache_latent_input,
-    caption_extention,
+    caption_extention_input,
-    enable_bucket,
+    enable_bucket_input,
     gradient_checkpointing,
-    full_fp16,
+    full_fp16_input,
-    no_token_padding,
+    no_token_padding_input,
-    stop_text_encoder_training,
+    stop_text_encoder_training_input,
-    use_8bit_adam,
+    use_8bit_adam_input,
-    xformers,
+    xformers_input,
-    save_model_as,
+    save_model_as_dropdown,
     shuffle_caption,
     save_state,
     resume,
@@ -189,70 +160,29 @@ def open_configuration(
     clip_skip,
     gradient_accumulation_steps,
     mem_eff_attn,
-    # vae,
+    output_name,
 ):
+    # Get list of function parameters and values
+    parameters = list(locals().items())
 
     original_file_path = file_path
     file_path = get_file_path(file_path)
-    # print(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 = json.load(f)
+            my_data_lora = 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 = {}
+        my_data_lora = {}
-
+    
-    # Return the values of the variables as a dictionary
+    values = [file_path]
-    return (
+    for key, value in parameters:
-        file_path,
+        # Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
-        my_data.get(
+        if not key in ['file_path']:
-            'pretrained_model_name_or_path', pretrained_model_name_or_path
+            values.append(my_data_lora.get(key, value))
-        ),
+    return tuple(values)
-        my_data.get('v2', v2),
-        my_data.get('v_parameterization', v_parameterization),
-        my_data.get('logging_dir', logging_dir),
-        my_data.get('train_data_dir', train_data_dir),
-        my_data.get('reg_data_dir', reg_data_dir),
-        my_data.get('output_dir', output_dir),
-        my_data.get('max_resolution', max_resolution),
-        my_data.get('lr_scheduler', lr_scheduler),
-        my_data.get('lr_warmup', lr_warmup),
-        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('cache_latent', cache_latent),
-        my_data.get('caption_extention', caption_extention),
-        my_data.get('enable_bucket', enable_bucket),
-        my_data.get('gradient_checkpointing', gradient_checkpointing),
-        my_data.get('full_fp16', full_fp16),
-        my_data.get('no_token_padding', no_token_padding),
-        my_data.get('stop_text_encoder_training', stop_text_encoder_training),
-        my_data.get('use_8bit_adam', use_8bit_adam),
-        my_data.get('xformers', xformers),
-        my_data.get('save_model_as', save_model_as),
-        my_data.get('shuffle_caption', shuffle_caption),
-        my_data.get('save_state', save_state),
-        my_data.get('resume', resume),
-        my_data.get('prior_loss_weight', prior_loss_weight),
-        my_data.get('text_encoder_lr', text_encoder_lr),
-        my_data.get('unet_lr', unet_lr),
-        my_data.get('network_dim', network_dim),
-        my_data.get('lora_network_weights', lora_network_weights),
-        my_data.get('color_aug', color_aug),
-        my_data.get('flip_aug', flip_aug),
-        my_data.get('clip_skip', clip_skip),
-        my_data.get('gradient_accumulation_steps', gradient_accumulation_steps),
-        my_data.get('mem_eff_attn', mem_eff_attn),
-        # my_data.get('vae', vae),
-    )
 
 
 def train_model(
@@ -296,23 +226,8 @@ def train_model(
     clip_skip,
     gradient_accumulation_steps,
     mem_eff_attn,
-    # vae,
+    output_name,
 ):
-    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',
-            )
-
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
         return
@@ -379,17 +294,6 @@ def train_model(
         # Print the result
         print(f'Folder {folder}: {steps} steps')
 
-    # Print the result
-    # print(f"{total_steps} total steps")
-
-    # if reg_data_dir == '':
-    #     reg_factor = 1
-    # else:
-    #     print(
-    #         'Regularisation images are used... Will double the number of steps required...'
-    #     )
-    #     reg_factor = 2
-
     # calculate max_train_steps
     max_train_steps = int(
         math.ceil(
@@ -496,68 +400,19 @@ def train_model(
         run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
     # if not vae == '':
     #     run_cmd += f' --vae="{vae}"'
+    if not output_name == '':
+        run_cmd += f' --output_name="{output_name}"'
 
     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')
+    last_dir = pathlib.Path(f'{output_dir}/{output_name}')
 
     if not last_dir.is_dir():
         # Copy inference model for v2 if required
-        save_inference_file(output_dir, v2, v_parameterization)
+        save_inference_file(output_dir, v2, v_parameterization, output_name)
-
-
-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 UI(username, password):
@@ -731,6 +586,13 @@ def lora_tab(
             logging_dir_input_folder.click(
                 get_folder_path, outputs=logging_dir_input
             )
+        with gr.Row():
+            output_name = gr.Textbox(
+                label='Model output name',
+                placeholder='Name of the model to output',
+                value='last',
+                interactive=True,
+            )
         train_data_dir_input.change(
             remove_doublequote,
             inputs=[train_data_dir_input],
@@ -766,7 +628,6 @@ def lora_tab(
                 outputs=lora_network_weights,
             )
         with gr.Row():
-            # learning_rate_input = gr.Textbox(label='Learning rate', value=1e-4, visible=False)
             lr_scheduler_input = gr.Dropdown(
                 label='LR Scheduler',
                 choices=[
@@ -941,7 +802,6 @@ def lora_tab(
         reg_data_dir_input,
         output_dir_input,
         max_resolution_input,
-        # learning_rate_input,
         lr_scheduler_input,
         lr_warmup_input,
         train_batch_size_input,
@@ -974,7 +834,7 @@ def lora_tab(
         clip_skip,
         gradient_accumulation_steps,
         mem_eff_attn,
-        # vae,
+        output_name,
     ]
 
     button_open_config.click(