fix: preserve sign of denominator in clamp to avoid inverting gradient direction

The previous .clamp(min=1e-6) on (sigma_ - sigma) flips the sign when
the denominator is negative (which is the typical case since sigmas
decrease monotonically). This would invert the target and cause
training divergence.

Use torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6) instead,
which prevents division by zero while preserving the correct sign.

diffsynth/diffusion/loss.py

@@ -91,7 +91,9 @@ class TrajectoryImitationLoss(torch.nn.Module):
                 progress_id_teacher = torch.argmin((timesteps_teacher - pipe.scheduler.timesteps[progress_id + 1]).abs())
                 latents_ = trajectory_teacher[progress_id_teacher]
             
-            target = (latents_ - inputs_shared["latents"]) / (sigma_ - sigma).clamp(min=1e-6)
+            denom = sigma_ - sigma
+            denom = torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6)
+            target = (latents_ - inputs_shared["latents"]) / denom
             loss = loss + torch.nn.functional.mse_loss(noise_pred.float(), target.float()) * pipe.scheduler.training_weight(timestep)
         return loss