Add VAE to meatada, add no_metadata option

networks/lora.py

@@ -178,6 +178,9 @@ class LoRANetwork(torch.nn.Module):
     return self.parameters()
 
   def save_weights(self, file, dtype, metadata):
+    if len(metadata) == 0:
+      metadata = None
+
     state_dict = self.state_dict()
 
     if dtype is not None:
@@ -191,5 +194,6 @@ class LoRANetwork(torch.nn.Module):
       save_file(state_dict, file, metadata)
     else:
       torch.save(state_dict, file)
+      if metadata is not None:
         with zipfile.ZipFile(file, "w") as zipf:
           zipf.writestr(LoRANetwork.METADATA_FILENAME, json.dumps(metadata))

train_network.py

@@ -201,7 +201,7 @@ def train(args):
       "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,
+      "ss_num_train_images": train_dataset.num_train_images,          # includes repeating TODO more detailed data
       "ss_num_reg_images": train_dataset.num_reg_images,
       "ss_num_batches_per_epoch": len(train_dataloader),
       "ss_num_epochs": num_train_epochs,
@@ -212,7 +212,8 @@ def train(args):
       "ss_lr_warmup_steps": args.lr_warmup_steps,
       "ss_lr_scheduler": args.lr_scheduler,
       "ss_network_module": args.network_module,
-    "ss_network_dim": 4 if args.network_dim is None else args.network_dim,
+      "ss_network_dim": args.network_dim,      # None means default because another network than LoRA may have another default dim
+      "ss_mixed_precision": args.mixed_precision,
       "ss_full_fp16": bool(args.full_fp16),
       "ss_v2": bool(args.v2),
       "ss_resolution": args.resolution,
@@ -223,12 +224,16 @@ 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(args.enable_bucket),
-    "ss_min_bucket_reso": args.min_bucket_reso,
+      "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
   }
 
+  # uncomment if another network is added
+  # for key, value in net_kwargs.items():
+  #   metadata["ss_arg_" + key] = value
+
   if args.pretrained_model_name_or_path is not None:
     sd_model_name = args.pretrained_model_name_or_path
     if os.path.exists(sd_model_name):
@@ -236,6 +241,13 @@ def train(args):
       sd_model_name = os.path.basename(sd_model_name)
     metadata["ss_sd_model_name"] = sd_model_name
 
+  if args.vae is not None:
+    vae_name = args.vae
+    if os.path.exists(vae_name):
+      metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
+      vae_name = os.path.basename(vae_name)
+    metadata["ss_vae_name"] = vae_name
+
   metadata = {k: str(v) for k, v in metadata.items()}
 
   progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
@@ -338,7 +350,7 @@ def train(args):
         ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as
         ckpt_file = os.path.join(args.output_dir, ckpt_name)
         print(f"saving checkpoint: {ckpt_file}")
-        unwrap_model(network).save_weights(ckpt_file, save_dtype, metadata)
+        unwrap_model(network).save_weights(ckpt_file, save_dtype, None if args.no_metadata else metadata)
 
       def remove_old_func(old_epoch_no):
         old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + '.' + args.save_model_as
@@ -374,7 +386,7 @@ def train(args):
     ckpt_file = os.path.join(args.output_dir, ckpt_name)
 
     print(f"save trained model to {ckpt_file}")
-    network.save_weights(ckpt_file, save_dtype, metadata)
+    network.save_weights(ckpt_file, save_dtype, None if args.no_metadata else metadata)
     print("model saved.")
 
 
@@ -385,6 +397,7 @@ if __name__ == '__main__':
   train_util.add_dataset_arguments(parser, True, True)
   train_util.add_training_arguments(parser, True)
 
+  parser.add_argument("--no_metadata", action='store_true', help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
   parser.add_argument("--save_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],
                       help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）")