Fix multi-gpu case for `train_cm_ct_unconditional.py` (#8653)

* Fix multi-gpu case

* Prefer previously created `unwrap_model()` function

For `torch.compile()` generalizability

* `chore: update unwrap_model() function to use accelerator.unwrap_model()`

examples/research_projects/consistency_training/train_cm_ct_unconditional.py

@@ -1195,7 +1195,7 @@ def main(args):
 
     # Resolve the c parameter for the Pseudo-Huber loss
     if args.huber_c is None:
-        args.huber_c = 0.00054 * args.resolution * math.sqrt(unet.config.in_channels)
+        args.huber_c = 0.00054 * args.resolution * math.sqrt(unwrap_model(unet).config.in_channels)
 
     # Get current number of discretization steps N according to our discretization curriculum
     current_discretization_steps = get_discretization_steps(