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[BUG] Fix Autoencoderkl train script #11113

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Merged
merged 3 commits into from
Mar 19, 2025
Merged

[BUG] Fix Autoencoderkl train script #11113

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678364c
add disc_optimizer step (not fix)
lavinal712 Mar 19, 2025
2e87bfc
support syncbatchnorm in discriminator
lavinal712 Mar 19, 2025
2800c97
Merge branch 'huggingface:main' into autoencoderkl_scripts
lavinal712 Mar 19, 2025
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12 changes: 10 additions & 2 deletions examples/research_projects/autoencoderkl/train_autoencoderkl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -627,6 +627,7 @@ def main(args):
ema_vae = EMAModel(vae.parameters(), model_cls=AutoencoderKL, model_config=vae.config)
perceptual_loss = lpips.LPIPS(net="vgg").eval()
discriminator = NLayerDiscriminator(input_nc=3, n_layers=3, use_actnorm=False).apply(weights_init)
discriminator = torch.nn.SyncBatchNorm.convert_sync_batchnorm(discriminator)
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Do we have to version-guard torch?

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No. As far as I know, this method first appeared in PyTorch 1.1.0 and is still valid in 2.6.0.

https://pytorch.org/docs/1.1.0/nn.html?highlight=syncbatchnorm#torch.nn.SyncBatchNorm


# Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
def unwrap_model(model):
Expand Down Expand Up @@ -951,13 +952,20 @@ def load_model_hook(models, input_dir):
logits_fake = discriminator(reconstructions)
disc_loss = hinge_d_loss if args.disc_loss == "hinge" else vanilla_d_loss
disc_factor = args.disc_factor if global_step >= args.disc_start else 0.0
disc_loss = disc_factor * disc_loss(logits_real, logits_fake)
d_loss = disc_factor * disc_loss(logits_real, logits_fake)
logs = {
"disc_loss": disc_loss.detach().mean().item(),
"disc_loss": d_loss.detach().mean().item(),
"logits_real": logits_real.detach().mean().item(),
"logits_fake": logits_fake.detach().mean().item(),
"disc_lr": disc_lr_scheduler.get_last_lr()[0],
}
accelerator.backward(d_loss)
if accelerator.sync_gradients:
params_to_clip = discriminator.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
disc_optimizer.step()
disc_lr_scheduler.step()
disc_optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
Expand Down
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