Skip to content

[BUG] Fix Autoencoderkl train script#11113

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
sayakpaul merged 3 commits into from
Mar 19, 2025
Merged

[BUG] Fix Autoencoderkl train script #11113

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 10 additions & 2 deletions examples/research_projects/autoencoderkl/train_autoencoderkl.py
View file
Open in desktop
Original file line numberDiff line numberDiff 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)

@sayakpaulsayakpaulMar 19, 2025

Copy link
Copy Markdown
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Do we have to version-guard torch?

@lavinal712lavinal712Mar 19, 2025

Copy link
Copy Markdown
ContributorAuthor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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 DownExpand 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