Fix issues found in review (pt 2)

Author: recris

library/train_util.py

@@ -5875,7 +5875,7 @@ def get_huber_threshold(args, timesteps: torch.Tensor, noise_scheduler) -> torch
         alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
         result = torch.exp(-alpha * timesteps) * args.huber_scale
     elif args.huber_schedule == "snr":
-        if not hasattr(noise_scheduler, "alphas_cumprod"):
+        if noise_scheduler is None or not hasattr(noise_scheduler, "alphas_cumprod"):
             raise NotImplementedError("Huber schedule 'snr' is not supported with the current model.")
         alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu())
         sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5

sd3_train.py

@@ -845,7 +845,7 @@ def grad_hook(parameter: torch.Tensor):
                 # )
                 # calculate loss
                 loss = train_util.conditional_loss(
-                    args, model_pred.float(), target.float(), timesteps, "none", noise_scheduler
+                    args, model_pred.float(), target.float(), timesteps, "none", None
                 )
                 if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
                     loss = apply_masked_loss(loss, batch)