Update README.md

support alpha etc.

README.md

@@ -2,58 +2,38 @@ This repository contains training, generation and utility scripts for Stable Dif
 
 ## Updates
 
-- 19 Jan. 2023, 2023/1/19
-  - Fix a part of LoRA modules are not trained when ``gradient_checkpointing`` is enabled. 
-  - Add ``--save_last_n_epochs_state`` option. You can specify how many state folders to keep, apart from how many models to keep. Thanks to shirayu!
-  - Fix Text Encoder training stops at ``max_train_steps`` even if ``max_train_epochs`` is set in `train_db.py``.
-  - Added script to check LoRA weights. You can check weights by ``python networks\check_lora_weights.py <model file>``. If some modules are not trained, the value is ``0.0`` like following. 
-    - ``lora_te_text_model_encoder_layers_11_*`` is not trained with ``clip_skip=2``, so ``0.0`` is okay for these modules.
-  - 一部のLoRAモジュールが ``gradient_checkpointing`` を有効にすると学習されない不具合を修正しました。ご不便をおかけしました。
-  - ``--save_last_n_epochs_state`` オプションを追加しました。モデルの保存数とは別に、stateフォルダの保存数を指定できます。shirayu氏に感謝します。
-  - ``train_db.py`` で、``max_train_epochs`` を指定していても、``max_train_steps`` のステップでText Encoderの学習が停止してしまう不具合を修正しました。
-  - LoRAの重みをチェックするスクリプトを追加してあります。``python networks\check_lora_weights.py <model file>`` のように実行してください。学習していない重みがあると、値が 下のように ``0.0`` になります。
-       - ``lora_te_text_model_encoder_layers_11_`` で始まる部分は ``clip_skip=2`` の場合は学習されないため、``0.0`` で正常です。
+__Stable Diffusion web UI now seems to support LoRA trained by ``sd-scripts``.__ Thank you for great work!!!
 
-- example result of ``check_lora_weights.py``, Text Encoder and a part of U-Net are not trained:
-```
-number of LoRA-up modules: 264
-lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight,0.0
-lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight,0.0
-lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight,0.0
-:
-lora_unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj.lora_up.weight,0.0
-lora_unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2.lora_up.weight,0.0
-lora_unet_mid_block_attentions_0_proj_in.lora_up.weight,0.003503334941342473
-lora_unet_mid_block_attentions_0_proj_out.lora_up.weight,0.004308608360588551
-:
-```
+Note: Currently the LoRA models trained by release 0.4.0 does not seem to be supported. If you use Web UI native LoRA support, please use release 0.3.2 for now. 
 
-- all modules are trained:
-```
-number of LoRA-up modules: 264
-lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight,0.0028684409335255623
-lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight,0.0029794853180646896
-lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight,0.002507600700482726
-lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_up.weight,0.002639499492943287
-:
-```
+The LoRA models for SD 2.x is not supported too.
 
-- 17 Jan. 2023, 2023/1/17
-  - __Important Notice__
-  It seems that only a part of LoRA modules are trained when ``gradient_checkpointing`` is enabled. The cause is under investigation, but for the time being, please train without ``gradient_checkpointing``. __The issue is fixed now.__
-  - __重要なお知らせ__
-  ``gradient_checkpointing`` を有効にすると LoRA モジュールの一部しか学習されないようです。原因は調査中ですが当面は ``gradient_checkpointing`` を指定せずに学習してください。__問題は修正されました。__
+- 22 Jan. 2023
+  - Add ``--network_alpha`` option to specify ``alpha`` value to prevent underflows for stable training. Thanks to CCRcmcpe!
+    - Details of the issue are described in https://github.com/kohya-ss/sd-webui-additional-networks/issues/49 .
+    - The default value is ``1``, scale ``1 / rank (or dimension)``. Set same value as ``network_dim`` for same behavior to old version.
+    - LoRA with a large dimension (rank) seems to require a higher learning rate with ``alpha=1`` (e.g. 1e-3 for 128-dim, still investigating).　
+  - Add logging for the learning rate for U-Net and Text Encoder independently, and for running average epoch loss. Thanks to mgz-dev!  
+  - Add more metadata such as dataset/reg image dirs, session ID, output name etc... See https://github.com/kohya-ss/sd-scripts/pull/77 for details. Thanks to space-nuko!
+    - __Now the metadata includes the folder name (the basename of the folder contains image files, not fullpath).__ If you do not want it, disable metadata storing with ``--no_metadata`` option.
+  - Add ``--training_comment`` option. You can specify an arbitrary string and refer to it by the extension.
 
-- 15 Jan. 2023, 2023/1/15
-  - Added ``--max_train_epochs`` and ``--max_data_loader_n_workers`` option for each training script. 
-  - If you specify the number of training epochs with ``--max_train_epochs``, the number of steps is calculated from the number of epochs automatically.
-  - You can set the number of workers for DataLoader with ``--max_data_loader_n_workers``, default is 8. The lower number may reduce the main memory usage and the time between epochs, but may cause slower dataloading (training).
-  - ``--max_train_epochs`` と ``--max_data_loader_n_workers`` のオプションが学習スクリプトに追加されました。
-  - ``--max_train_epochs`` で学習したいエポック数を指定すると、必要なステップ数が自動的に計算され設定されます。
-  - ``--max_data_loader_n_workers`` で DataLoader の worker 数が指定できます（デフォルトは8）。値を小さくするとメインメモリの使用量が減り、エポック間の待ち時間も短くなるようです。ただしデータ読み込み（学習時間）は長くなる可能性があります。
+Stable Diffusion web UI本体で当リポジトリで学習したLoRAモデルによる画像生成がサポートされたようです。
 
-Please read [release version 0.3.0](https://github.com/kohya-ss/sd-scripts/releases/tag/v0.3.0) for recent updates.
-最近の更新情報は [release version 0.3.0](https://github.com/kohya-ss/sd-scripts/releases/tag/v0.3.0) をご覧ください。
+注：現時点ではversion 0.4.0で学習したモデルはサポートされないようです。Web UI本体の生成機能を使う場合には、version 0.3.2を引き続きご利用ください。またSD2.x用のLoRAモデルもサポートされないようです。
+
+- 2023/1/22
+  - アンダーフローを防ぎ安定して学習するための ``alpha`` 値を指定する、``--network_alpha`` オプションを追加しました。CCRcmcpe 氏に感謝します。
+    - 問題の詳細はこちらをご覧ください： https://github.com/kohya-ss/sd-webui-additional-networks/issues/49
+    - デフォルト値は ``1`` で、LoRAの計算結果を ``1 / rank (dimension・次元数)`` 倍します（つまり小さくなります。これにより同じ効果を出すために必要なLoRAの重みの変化が大きくなるため、アンダーフローが避けられるようになります）。``network_dim`` と同じ値を指定すると旧バージョンと同じ動作になります。
+    -  ``alpha=1``の場合、次元数（rank）の多いLoRAモジュールでは学習率を高めにしたほうが良いようです（128次元で1e-3など）。
+  - U-Net と Text Encoder のそれぞれの学習率、エポックの平均lossをログに記録するようになりました。mgz-dev 氏に感謝します。
+  - 画像ディレクトリ、セッションID、出力名などいくつかの項目がメタデータに追加されました（詳細は https://github.com/kohya-ss/sd-scripts/pull/77 を参照）。space-nuko氏に感謝します。
+    - __メタデータにフォルダ名が含まれるようになりました（画像を含むフォルダの名前のみで、フルパスではありません）。__ もし望まない場合には ``--no_metadata`` オプションでメタデータの記録を止めてください。
+  - ``--training_comment`` オプションを追加しました。任意の文字列を指定でき、Web UI拡張から参照できます。
+
+Please read [Releases](https://github.com/kohya-ss/sd-scripts/releases) for recent updates.
+最近の更新情報は [Release](https://github.com/kohya-ss/sd-scripts/releases) をご覧ください。
 
 ##
 

gen_img_diffusers.py

@@ -1981,7 +1981,6 @@ def main(args):
       imported_module = importlib.import_module(network_module)
 
       network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
-      network_dim = None if args.network_dim is None or len(args.network_dim) <= i else args.network_dim[i]
 
       net_kwargs = {}
       if args.network_args and i < len(args.network_args):
@@ -1992,22 +1991,22 @@ def main(args):
           key, value = net_arg.split("=")
           net_kwargs[key] = value
 
-      network = imported_module.create_network(network_mul, network_dim, vae, text_encoder, unet, **net_kwargs)
-      if network is None:
-        return
-
       if args.network_weights and i < len(args.network_weights):
         network_weight = args.network_weights[i]
         print("load network weights from:", network_weight)
 
-        if os.path.splitext(network_weight)[1] == '.safetensors':
+        if model_util.is_safetensors(network_weight):
           from safetensors.torch import safe_open
           with safe_open(network_weight, framework="pt") as f:
             metadata = f.metadata()
           if metadata is not None:
             print(f"metadata for: {network_weight}: {metadata}")
 
-        network.load_weights(network_weight)
+        network = imported_module.create_network_from_weights(network_mul, network_weight, vae, text_encoder, unet, **net_kwargs)
+      else:
+        raise ValueError("No weight. Weight is required.")
+      if network is None:
+        return
 
       network.apply_to(text_encoder, unet)
 
@@ -2526,8 +2525,6 @@ if __name__ == '__main__':
   parser.add_argument("--network_weights", type=str, default=None, nargs='*',
                       help='Hypernetwork weights to load / Hypernetworkの重み')
   parser.add_argument("--network_mul", type=float, default=None, nargs='*', help='Hypernetwork multiplier / Hypernetworkの効果の倍率')
-  parser.add_argument("--network_dim", type=int, default=None, nargs='*',
-                      help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
   parser.add_argument("--network_args", type=str, default=None, nargs='*',
                       help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
   parser.add_argument("--clip_skip", type=int, default=None, help='layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う')

library/train_util.py

@@ -11,6 +11,7 @@ import glob
 import math
 import os
 import random
+import hashlib
 
 from tqdm import tqdm
 import torch
@@ -79,6 +80,11 @@ class BaseDataset(torch.utils.data.Dataset):
     self.debug_dataset = debug_dataset
     self.random_crop = random_crop
     self.token_padding_disabled = False
+    self.dataset_dirs_info = {}
+    self.reg_dataset_dirs_info = {}
+    self.enable_bucket = False
+    self.min_bucket_reso = None
+    self.max_bucket_reso = None
 
     self.tokenizer_max_length = self.tokenizer.model_max_length if max_token_length is None else max_token_length + 2
 
@@ -463,6 +469,8 @@ class DreamBoothDataset(BaseDataset):
       assert max(resolution) <= max_bucket_reso, f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください"
       self.bucket_resos, self.bucket_aspect_ratios = model_util.make_bucket_resolutions(
           (self.width, self.height), min_bucket_reso, max_bucket_reso)
+      self.min_bucket_reso = min_bucket_reso
+      self.max_bucket_reso = max_bucket_reso
     else:
       self.bucket_resos = [(self.width, self.height)]
       self.bucket_aspect_ratios = [self.width / self.height]
@@ -523,6 +531,7 @@ class DreamBoothDataset(BaseDataset):
       for img_path, caption in zip(img_paths, captions):
         info = ImageInfo(img_path, n_repeats, caption, False, img_path)
         self.register_image(info)
+      self.dataset_dirs_info[os.path.basename(dir)] = {"n_repeats": n_repeats, "img_count": len(img_paths)}
     print(f"{num_train_images} train images with repeating.")
     self.num_train_images = num_train_images
 
@@ -539,6 +548,7 @@ class DreamBoothDataset(BaseDataset):
         for img_path, caption in zip(img_paths, captions):
           info = ImageInfo(img_path, n_repeats, caption, True, img_path)
           reg_infos.append(info)
+        self.reg_dataset_dirs_info[os.path.basename(dir)] = {"n_repeats": n_repeats, "img_count": len(img_paths)}
 
       print(f"{num_reg_images} reg images.")
       if num_train_images < num_reg_images:
@@ -611,6 +621,8 @@ class FineTuningDataset(BaseDataset):
     self.num_train_images = len(metadata) * dataset_repeats
     self.num_reg_images = 0
 
+    self.dataset_dirs_info[os.path.basename(self.train_data_dir)] = {"n_repeats": dataset_repeats, "img_count": len(metadata)}
+
     # check existence of all npz files
     if not self.color_aug:
       npz_any = False
@@ -653,6 +665,8 @@ class FineTuningDataset(BaseDataset):
         assert max(resolution) <= max_bucket_reso, f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください"
         self.bucket_resos, self.bucket_aspect_ratios = model_util.make_bucket_resolutions(
             (self.width, self.height), min_bucket_reso, max_bucket_reso)
+        self.min_bucket_reso = min_bucket_reso
+        self.max_bucket_reso = max_bucket_reso
       else:
         self.bucket_resos = [(self.width, self.height)]
         self.bucket_aspect_ratios = [self.width / self.height]
@@ -665,6 +679,9 @@ class FineTuningDataset(BaseDataset):
       self.bucket_resos.sort()
       self.bucket_aspect_ratios = [w / h for w, h in self.bucket_resos]
 
+      self.min_bucket_reso = min([min(reso) for reso in resos])
+      self.max_bucket_reso = max([max(reso) for reso in resos])
+
   def image_key_to_npz_file(self, image_key):
     base_name = os.path.splitext(image_key)[0]
     npz_file_norm = base_name + '.npz'
@@ -749,9 +766,9 @@ def default(val, d):
 
 
 def model_hash(filename):
+  """Old model hash used by stable-diffusion-webui"""
   try:
     with open(filename, "rb") as file:
-      import hashlib
       m = hashlib.sha256()
 
       file.seek(0x100000)
@@ -761,6 +778,18 @@ def model_hash(filename):
     return 'NOFILE'
 
 
+def calculate_sha256(filename):
+  """New model hash used by stable-diffusion-webui"""
+  hash_sha256 = hashlib.sha256()
+  blksize = 1024 * 1024
+
+  with open(filename, "rb") as f:
+    for chunk in iter(lambda: f.read(blksize), b""):
+      hash_sha256.update(chunk)
+
+  return hash_sha256.hexdigest()
+
+
 # flash attention forwards and backwards
 
 # https://arxiv.org/abs/2205.14135
@@ -1029,7 +1058,8 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
   parser.add_argument("--save_every_n_epochs", type=int, default=None,
                       help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する")
   parser.add_argument("--save_last_n_epochs", type=int, default=None, help="save last N checkpoints / 最大Nエポック保存する")
-  parser.add_argument("--save_last_n_epochs_state", type=int, default=None, help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する(--save_last_n_epochsの指定を上書きします)")
+  parser.add_argument("--save_last_n_epochs_state", type=int, default=None,
+                      help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する(--save_last_n_epochsの指定を上書きします)")
   parser.add_argument("--save_state", action="store_true",
                       help="save training state additionally (including optimizer states etc.) / optimizerなど学習状態も含めたstateを追加で保存する")
   parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate")
@@ -1048,8 +1078,10 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
 
   parser.add_argument("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率")
   parser.add_argument("--max_train_steps", type=int, default=1600, help="training steps / 学習ステップ数")
-  parser.add_argument("--max_train_epochs", type=int, default=None, help="training epochs (overrides max_train_steps) / 学習エポック数（max_train_stepsを上書きします）")
-  parser.add_argument("--max_data_loader_n_workers", type=int, default=8, help="max num workers for DataLoader (lower is less main RAM usage, faster epoch start and slower data loading) / DataLoaderの最大プロセス数（小さい値ではメインメモリの使用量が減りエポック間の待ち時間が減りますが、データ読み込みは遅くなります）")
+  parser.add_argument("--max_train_epochs", type=int, default=None,
+                      help="training epochs (overrides max_train_steps) / 学習エポック数（max_train_stepsを上書きします）")
+  parser.add_argument("--max_data_loader_n_workers", type=int, default=8,
+                      help="max num workers for DataLoader (lower is less main RAM usage, faster epoch start and slower data loading) / DataLoaderの最大プロセス数（小さい値ではメインメモリの使用量が減りエポック間の待ち時間が減りますが、データ読み込みは遅くなります）")
   parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed")
   parser.add_argument("--gradient_checkpointing", action="store_true",
                       help="enable gradient checkpointing / grandient checkpointingを有効にする")

networks/check_lora_weights.py

@@ -15,12 +15,13 @@ def main(file):
 
   keys = list(sd.keys())
   for key in keys:
-    if 'lora_up' in key:
+    if 'lora_up' in key or 'lora_down' in key:
       values.append((key, sd[key]))
-  print(f"number of LoRA-up modules: {len(values)}")
+  print(f"number of LoRA modules: {len(values)}")
 
   for key, value in values:
-    print(f"{key},{torch.mean(torch.abs(value))}")
+    value = value.to(torch.float32)
+    print(f"{key},{torch.mean(torch.abs(value))},{torch.min(torch.abs(value))}")
 
 
 if __name__ == '__main__':

networks/extract_lora_from_models.py

@@ -44,9 +44,9 @@ def svd(args):
   print(f"loading SD model : {args.model_tuned}")
   text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned)
 
-  # create LoRA network to extract weights
-  lora_network_o = lora.create_network(1.0, args.dim, None, text_encoder_o, unet_o)
-  lora_network_t = lora.create_network(1.0, args.dim, None, text_encoder_t, unet_t)
+  # create LoRA network to extract weights: Use dim (rank) as alpha
+  lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_o, unet_o)
+  lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_t, unet_t)
   assert len(lora_network_o.text_encoder_loras) == len(
       lora_network_t.text_encoder_loras), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） "
 
@@ -77,10 +77,10 @@ def svd(args):
     module_t = lora_t.org_module
     diff = module_t.weight - module_o.weight
     diff = diff.float()
-    
+
     if args.device:
       diff = diff.to(args.device)
-      
+
     diffs[lora_name] = diff
 
   # make LoRA with svd
@@ -116,6 +116,9 @@ def svd(args):
   print(f"LoRA has {len(lora_sd)} weights.")
 
   for key in list(lora_sd.keys()):
+    if "alpha" in key:
+      continue
+
     lora_name = key.split('.')[0]
     i = 0 if "lora_up" in key else 1
 
@@ -124,7 +127,7 @@ def svd(args):
     if len(lora_sd[key].size()) == 4:
       weights = weights.unsqueeze(2).unsqueeze(3)
 
-    assert weights.size() == lora_sd[key].size()
+    assert weights.size() == lora_sd[key].size(), f"size unmatch: {key}"
     lora_sd[key] = weights
 
   # load state dict to LoRA and save it
@@ -135,7 +138,10 @@ def svd(args):
   if dir_name and not os.path.exists(dir_name):
     os.makedirs(dir_name, exist_ok=True)
 
-  lora_network_o.save_weights(args.save_to, save_dtype, {})
+  # minimum metadata
+  metadata = {"ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)}
+
+  lora_network_o.save_weights(args.save_to, save_dtype, metadata)
   print(f"LoRA weights are saved to: {args.save_to}")
 
 
@@ -151,8 +157,8 @@ if __name__ == '__main__':
                       help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors")
   parser.add_argument("--save_to", type=str, default=None,
                       help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
-  parser.add_argument("--dim", type=int, default=4, help="dimension of LoRA (default 4) / LoRAの次元数（デフォルト4）")
-  parser.add_argument("--device", type=str, default=None, help="device to use, 'cuda' for GPU / 計算を行うデバイス、'cuda'でGPUを使う")
+  parser.add_argument("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
+  parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
 
   args = parser.parse_args()
   svd(args)

networks/lora.py

@@ -13,9 +13,11 @@ class LoRAModule(torch.nn.Module):
   replaces forward method of the original Linear, instead of replacing the original Linear module.
   """
 
-  def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4):
+  def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
+    """ if alpha == 0 or None, alpha is rank (no scaling). """
     super().__init__()
     self.lora_name = lora_name
+    self.lora_dim = lora_dim
 
     if org_module.__class__.__name__ == 'Conv2d':
       in_dim = org_module.in_channels
@@ -28,6 +30,12 @@ class LoRAModule(torch.nn.Module):
       self.lora_down = torch.nn.Linear(in_dim, lora_dim, bias=False)
       self.lora_up = torch.nn.Linear(lora_dim, out_dim, bias=False)
 
+    if type(alpha) == torch.Tensor:
+      alpha = alpha.detach().numpy()
+    alpha = lora_dim if alpha is None or alpha == 0 else alpha
+    self.scale = alpha / self.lora_dim
+    self.register_buffer('alpha', torch.tensor(alpha))                    # 定数として扱える
+
     # same as microsoft's
     torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
     torch.nn.init.zeros_(self.lora_up.weight)
@@ -41,13 +49,37 @@ class LoRAModule(torch.nn.Module):
     del self.org_module
 
   def forward(self, x):
-    return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier
+    return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
 
 
-def create_network(multiplier, network_dim, vae, text_encoder, unet, **kwargs):
+def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
   if network_dim is None:
     network_dim = 4                     # default
-  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim)
+  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
+  return network
+
+
+def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwargs):
+  if os.path.splitext(file)[1] == '.safetensors':
+    from safetensors.torch import load_file, safe_open
+    weights_sd = load_file(file)
+  else:
+    weights_sd = torch.load(file, map_location='cpu')
+
+  # get dim (rank)
+  network_alpha = None
+  network_dim = None
+  for key, value in weights_sd.items():
+    if network_alpha is None and 'alpha' in key:
+      network_alpha = value
+    if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
+      network_dim = value.size()[0]
+
+  if network_alpha is None:
+    network_alpha = network_dim
+
+  network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha)
+  network.weights_sd = weights_sd
   return network
 
 
@@ -57,10 +89,11 @@ class LoRANetwork(torch.nn.Module):
   LORA_PREFIX_UNET = 'lora_unet'
   LORA_PREFIX_TEXT_ENCODER = 'lora_te'
 
-  def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4) -> None:
+  def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4, alpha=1) -> None:
     super().__init__()
     self.multiplier = multiplier
     self.lora_dim = lora_dim
+    self.alpha = alpha
 
     # create module instances
     def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]:
@@ -71,7 +104,7 @@ class LoRANetwork(torch.nn.Module):
             if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
               lora_name = prefix + '.' + name + '.' + child_name
               lora_name = lora_name.replace('.', '_')
-              lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim)
+              lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim, self.alpha)
               loras.append(lora)
       return loras
 
@@ -149,21 +182,21 @@ class LoRANetwork(torch.nn.Module):
       return params
 
     self.requires_grad_(True)
-    params = []
+    all_params = []
 
     if self.text_encoder_loras:
       param_data = {'params': enumerate_params(self.text_encoder_loras)}
       if text_encoder_lr is not None:
         param_data['lr'] = text_encoder_lr
-      params.append(param_data)
+      all_params.append(param_data)
 
     if self.unet_loras:
       param_data = {'params': enumerate_params(self.unet_loras)}
       if unet_lr is not None:
         param_data['lr'] = unet_lr
-      params.append(param_data)
+      all_params.append(param_data)
 
-    return params
+    return all_params
 
   def prepare_grad_etc(self, text_encoder, unet):
     self.requires_grad_(True)

networks/merge_lora.py

@@ -61,6 +61,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
     for key in lora_sd.keys():
       if "lora_down" in key:
         up_key = key.replace("lora_down", "lora_up")
+        alpha_key = key[:key.index("lora_down")] + 'alpha'
 
         # find original module for this lora
         module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
@@ -73,14 +74,18 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
         down_weight = lora_sd[key]
         up_weight = lora_sd[up_key]
 
+        dim = down_weight.size()[0]
+        alpha = lora_sd.get(alpha_key, dim)
+        scale = alpha / dim
+
         # W <- W + U * D
         weight = module.weight
         if len(weight.size()) == 2:
           # linear
-          weight = weight + ratio * (up_weight @ down_weight)
+          weight = weight + ratio * (up_weight @ down_weight) * scale
         else:
           # conv2d
-          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale
 
         module.weight = torch.nn.Parameter(weight)
 
@@ -88,20 +93,35 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
 def merge_lora_models(models, ratios, merge_dtype):
   merged_sd = {}
 
+  alpha = None
+  dim = None
   for model, ratio in zip(models, ratios):
     print(f"loading: {model}")
     lora_sd = load_state_dict(model, merge_dtype)
 
     print(f"merging...")
     for key in lora_sd.keys():
-      if key in merged_sd:
-        assert merged_sd[key].size() == lora_sd[key].size(
-        ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
-        merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
+      if 'alpha' in key:
+        if key in merged_sd:
+          assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません"
+        else:
+          alpha = lora_sd[key].detach().numpy()
+          merged_sd[key] = lora_sd[key]
       else:
-        merged_sd[key] = lora_sd[key] * ratio
+        if key in merged_sd:
+          assert merged_sd[key].size() == lora_sd[key].size(
+          ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
+          merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
+        else:
+          if "lora_down" in key:
+            dim = lora_sd[key].size()[0]
+          merged_sd[key] = lora_sd[key] * ratio
 
-  return merged_sd
+  print(f"dim (rank): {dim}, alpha: {alpha}")
+  if alpha is None:
+    alpha = dim
+
+  return merged_sd, dim, alpha
 
 
 def merge(args):
@@ -132,7 +152,7 @@ def merge(args):
     model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
                                                 args.sd_model, 0, 0, save_dtype, vae)
   else:
-    state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
+    state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
 
     print(f"saving model to: {args.save_to}")
     save_to_file(args.save_to, state_dict, state_dict, save_dtype)
@@ -145,7 +165,7 @@ if __name__ == '__main__':
   parser.add_argument("--save_precision", type=str, default=None,
                       choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
   parser.add_argument("--precision", type=str, default="float",
-                      choices=["float", "fp16", "bf16"], help="precision in merging / マージの計算時の精度")
+                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
   parser.add_argument("--sd_model", type=str, default=None,
                       help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
   parser.add_argument("--save_to", type=str, default=None,

train_network.py

@@ -3,6 +3,9 @@ import argparse
 import gc
 import math
 import os
+import random
+import time
+import json
 
 from tqdm import tqdm
 import torch
@@ -18,7 +21,23 @@ def collate_fn(examples):
   return examples[0]
 
 
+def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
+  logs = {"loss/current": current_loss, "loss/average": avr_loss}
+
+  if args.network_train_unet_only:
+    logs["lr/unet"] = lr_scheduler.get_last_lr()[0]
+  elif args.network_train_text_encoder_only:
+    logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
+  else:
+    logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
+    logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]          # may be same to textencoder
+
+  return logs
+
+
 def train(args):
+  session_id = random.randint(0, 2**32)
+  training_started_at = time.time()
   train_util.verify_training_args(args)
   train_util.prepare_dataset_args(args, True)
 
@@ -88,7 +107,8 @@ def train(args):
       key, value = net_arg.split('=')
       net_kwargs[key] = value
 
-  network = network_module.create_network(1.0, args.network_dim, vae, text_encoder, unet, **net_kwargs)
+  # if a new network is added in future, add if ~ then blocks for each network (;'∀')
+  network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
   if network is None:
     return
 
@@ -206,21 +226,26 @@ def train(args):
   print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
   metadata = {
+      "ss_session_id": session_id,            # random integer indicating which group of epochs the model came from
+      "ss_training_started_at": training_started_at,          # unix timestamp
+      "ss_output_name": args.output_name,
       "ss_learning_rate": args.learning_rate,
       "ss_text_encoder_lr": args.text_encoder_lr,
       "ss_unet_lr": args.unet_lr,
-      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating TODO more detailed data
+      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating
       "ss_num_reg_images": train_dataset.num_reg_images,
       "ss_num_batches_per_epoch": len(train_dataloader),
       "ss_num_epochs": num_train_epochs,
       "ss_batch_size_per_device": args.train_batch_size,
       "ss_total_batch_size": total_batch_size,
+      "ss_gradient_checkpointing": args.gradient_checkpointing,
       "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
       "ss_max_train_steps": args.max_train_steps,
       "ss_lr_warmup_steps": args.lr_warmup_steps,
       "ss_lr_scheduler": args.lr_scheduler,
       "ss_network_module": args.network_module,
-      "ss_network_dim": args.network_dim,      # None means default because another network than LoRA may have another default dim
+      "ss_network_dim": args.network_dim,          # None means default because another network than LoRA may have another default dim
+      "ss_network_alpha": args.network_alpha,      # some networks may not use this value
       "ss_mixed_precision": args.mixed_precision,
       "ss_full_fp16": bool(args.full_fp16),
       "ss_v2": bool(args.v2),
@@ -232,10 +257,14 @@ def train(args):
       "ss_random_crop": bool(args.random_crop),
       "ss_shuffle_caption": bool(args.shuffle_caption),
       "ss_cache_latents": bool(args.cache_latents),
-      "ss_enable_bucket": bool(train_dataset.enable_bucket),        # TODO move to BaseDataset from DB/FT
-      "ss_min_bucket_reso": args.min_bucket_reso,                   # TODO get from dataset
-      "ss_max_bucket_reso": args.max_bucket_reso,
-      "ss_seed": args.seed
+      "ss_enable_bucket": bool(train_dataset.enable_bucket),
+      "ss_min_bucket_reso": train_dataset.min_bucket_reso,
+      "ss_max_bucket_reso": train_dataset.max_bucket_reso,
+      "ss_seed": args.seed,
+      "ss_keep_tokens": args.keep_tokens,
+      "ss_dataset_dirs": json.dumps(train_dataset.dataset_dirs_info),
+      "ss_reg_dataset_dirs": json.dumps(train_dataset.reg_dataset_dirs_info),
+      "ss_training_comment": args.training_comment        # will not be updated after training
   }
 
   # uncomment if another network is added
@@ -246,6 +275,7 @@ def train(args):
     sd_model_name = args.pretrained_model_name_or_path
     if os.path.exists(sd_model_name):
       metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
+      metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name)
       sd_model_name = os.path.basename(sd_model_name)
     metadata["ss_sd_model_name"] = sd_model_name
 
@@ -253,6 +283,7 @@ def train(args):
     vae_name = args.vae
     if os.path.exists(vae_name):
       metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
+      metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name)
       vae_name = os.path.basename(vae_name)
     metadata["ss_vae_name"] = vae_name
 
@@ -333,20 +364,20 @@ def train(args):
         global_step += 1
 
       current_loss = loss.detach().item()
-      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 args.logging_dir is not None:
+        logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
+        accelerator.log(logs, step=global_step)
+
       if global_step >= args.max_train_steps:
         break
 
     if args.logging_dir is not None:
-      logs = {"epoch_loss": loss_total / len(train_dataloader)}
+      logs = {"loss/epoch": loss_total / len(train_dataloader)}
       accelerator.log(logs, step=epoch+1)
 
     accelerator.wait_for_everyone()
@@ -417,11 +448,15 @@ if __name__ == '__main__':
   parser.add_argument("--network_module", type=str, default=None, help='network module to train / 学習対象のネットワークのモジュール')
   parser.add_argument("--network_dim", type=int, default=None,
                       help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
+  parser.add_argument("--network_alpha", type=float, default=1,
+                      help='alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）')
   parser.add_argument("--network_args", type=str, default=None, nargs='*',
                       help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
   parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
   parser.add_argument("--network_train_text_encoder_only", action="store_true",
                       help="only training Text Encoder part / Text Encoder関連部分のみ学習する")
+  parser.add_argument("--training_comment", type=str, default=None,
+                      help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列")
 
   args = parser.parse_args()
   train(args)