update FLUX LoRA training

library/train_util.py

@@ -3186,6 +3186,7 @@ def get_sai_model_spec(
     textual_inversion: bool,
     is_stable_diffusion_ckpt: Optional[bool] = None,  # None for TI and LoRA
     sd3: str = None,
+    flux: str = None,
 ):
     timestamp = time.time()
 
@@ -3220,6 +3221,7 @@ def get_sai_model_spec(
         timesteps=timesteps,
         clip_skip=args.clip_skip,  # None or int
         sd3=sd3,
+        flux=flux,
     )
     return metadata
 
@@ -3642,8 +3644,8 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         "--loss_type",
         type=str,
         default="l2",
-        choices=["l2", "huber", "smooth_l1"],
-        help="The type of loss function to use (L2, Huber, or smooth L1), default is L2 / 使用する損失関数の種類（L2、Huber、またはsmooth L1）、デフォルトはL2",
+        choices=["l1", "l2", "huber", "smooth_l1"],
+        help="The type of loss function to use (L1, L2, Huber, or smooth L1), default is L2 / 使用する損失関数の種類（L1、L2、Huber、またはsmooth L1）、デフォルトはL2",
     )
     parser.add_argument(
         "--huber_schedule",
@@ -5359,9 +5361,10 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
 def conditional_loss(
     model_pred: torch.Tensor, target: torch.Tensor, reduction: str = "mean", loss_type: str = "l2", huber_c: float = 0.1
 ):
-
     if loss_type == "l2":
         loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction)
+    elif loss_type == "l1":
+        loss = torch.nn.functional.l1_loss(model_pred, target, reduction=reduction)
     elif loss_type == "huber":
         loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)
         if reduction == "mean":