feat: support wandb logging

fine_tune.py

@@ -260,7 +260,7 @@ def train(args):
     )
 
     if accelerator.is_main_process:
-        accelerator.init_trackers("finetuning")
+        accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name)
 
     for epoch in range(num_train_epochs):
         print(f"epoch {epoch+1}/{num_train_epochs}")

library/train_util.py

@@ -2067,7 +2067,15 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="enable logging and output TensorBoard log to this directory / ログ出力を有効にしてこのディレクトリにTensorBoard用のログを出力する",
     )
+    parser.add_argument(
+        "--log_with",
+        type=str,
+        default=None,
+        choices=["tensorboard", "wandb", "all"],
+        help="what logging tool(s) to use (if 'all', TensorBoard and WandB are both used) / ログ出力に使用するツール (allを指定するとTensorBoardとWandBの両方が使用される)",
+    )
     parser.add_argument("--log_prefix", type=str, default=None, help="add prefix for each log directory / ログディレクトリ名の先頭に追加する文字列")
+    parser.add_argument("--log_tracker_name", type=str, default=None, help="name of tracker to use for logging / ログ出力に使用するtrackerの名前")
     parser.add_argument(
         "--noise_offset",
         type=float,
@@ -2735,7 +2743,12 @@ def prepare_accelerator(args: argparse.Namespace):
         log_with = None
         logging_dir = None
     else:
-        log_with = "tensorboard"
+        log_with = "tensorboard" if args.log_with is None else args.log_with
+        if log_with in ["wandb", "all"]:
+            try:
+                import wandb
+            except ImportError:
+                raise ImportError("No wandb / wandb がインストールされていないようです")
         log_prefix = "" if args.log_prefix is None else args.log_prefix
         logging_dir = args.logging_dir + "/" + log_prefix + time.strftime("%Y%m%d%H%M%S", time.localtime())
 
@@ -3197,6 +3210,20 @@ def sample_images(
 
                 image.save(os.path.join(save_dir, img_filename))
 
+                # wandb有効時のみログを送信
+                try:
+                    wandb_tracker = accelerator.get_tracker("wandb")
+                    try:
+                        import wandb
+                    except ImportError: # 事前に一度確認するのでここはエラー出ないはず
+                        raise ImportError("No wandb / wandb がインストールされていないようです")
+                    
+                    wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
+                except: # wandb 無効時
+                    pass
+                
+                
+
     # clear pipeline and cache to reduce vram usage
     del pipeline
     torch.cuda.empty_cache()
@@ -3205,7 +3232,6 @@ def sample_images(
     torch.cuda.set_rng_state(cuda_rng_state)
     vae.to(org_vae_device)
 
-
 # endregion
 
 # region 前処理用

train_db.py

@@ -231,7 +231,7 @@ def train(args):
     )
 
     if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth")
+        accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name)
 
     loss_list = []
     loss_total = 0.0

train_network.py

@@ -538,7 +538,7 @@ def train(args):
         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("network_train")
+        accelerator.init_trackers("network_train" if args.log_tracker_name is None else args.log_tracker_name)
 
     loss_list = []
     loss_total = 0.0

train_textual_inversion.py

@@ -337,7 +337,7 @@ def train(args):
     )
 
     if accelerator.is_main_process:
-        accelerator.init_trackers("textual_inversion")
+        accelerator.init_trackers("textual_inversion" if args.log_tracker_name is None else args.log_tracker_name)
 
     for epoch in range(num_train_epochs):
         print(f"epoch {epoch+1}/{num_train_epochs}")

train_textual_inversion_XTI.py

@@ -371,7 +371,7 @@ def train(args):
     )
 
     if accelerator.is_main_process:
-        accelerator.init_trackers("textual_inversion")
+        accelerator.init_trackers("textual_inversion" if args.log_tracker_name is None else args.log_tracker_name)
 
     for epoch in range(num_train_epochs):
         print(f"epoch {epoch+1}/{num_train_epochs}")