LayerSync

Image Generation

LayerSync improves generation quality without relying on external representation. We compare the images generated by SiT-XL/2 when regularized with dispersive and LayerSync. All the models are trained on ImageNet 256×256 for 400K iterations, share the same noise, sampler, and number of sampling steps, and none of them use classifier-free guidance.

Audio Generation

Side-by-side comparison of audio samples generated with Baseline vs LayerSync. Both models are trained for the same number of iterations, share the same noise, sampler, and number of sampling steps, and none of them use classifier-free guidance.

Samples: 01 02 03 04 05 06 07

Sample 01

Baseline

LayerSync

Sample 02

Baseline

LayerSync

Sample 03

Baseline

LayerSync

Sample 04

Baseline

LayerSync

Sample 05

Baseline

LayerSync

Sample 06

Baseline

LayerSync

Sample 07

Baseline

LayerSync

Evolution over Training

The evolution of audio generation over different epoches.

Baseline

Epoch 280

Epoch 475

Epoch 650

Baseline + LayerSync

Epoch 280

Epoch 475

Epoch 650

Text-Conditioned Human Motion Generation

Qualitative comparison between human motions generated with MDM and MDM + LayerSync. The condition text is randomly selected from HumanML3D test set, both models are trained for the same number of iterations and the generated samples share the same noise.

MDM

MDM + LayerSync

MDM

MDM + LayerSync

MDM

MDM + LayerSync

MDM

MDM + LayerSync

Video Generation

Training on CLEVRER

Qualitative comparision for unconditional video generation on CLEVRER dataset between baseline and baseline + LayerSync. Both models are trained for the same number of iterations.

Baseline