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

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* Fix multi-gpu case

* Prefer previously created `unwrap_model()` function

For `torch.compile()` generalizability

* `chore: update unwrap_model() function to use accelerator.unwrap_model()`
This commit is contained in:
Tolga Cangöz
2024-07-17 16:33:12 +03:00
committed by GitHub
parent e15a8e7f17
commit c1dc2ae619
1 changed files with 1 additions and 1 deletions
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examples/research_projects/consistency_training/train_cm_ct_unconditional.py
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@@ -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(