The content introduces Latent Adversarial Diffusion Distillation (LADD) as a novel approach to overcome the limitations of existing methods like ADD. LADD simplifies training by utilizing generative features from pretrained latent diffusion models, enabling high-resolution multi-aspect ratio image synthesis. By leveraging a lower-dimensional latent space, LADD significantly reduces memory requirements and facilitates efficient scaling to large model sizes and high resolutions. The method eliminates the need for decoding back to the image space, reducing memory demands compared to its predecessor. Additionally, LADD offers structured feedback at different noise levels, allowing for better control over discriminator behavior. The content also discusses the unification of teacher and discriminator models in latent space, synthetic data generation with teacher models, and the benefits of using generative features over discriminative ones in adversarial training.
In un'altra lingua
dal contenuto originale
arxiv.org
Approfondimenti chiave tratti da
by Axel Sauer,F... alle arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.12015.pdfDomande più approfondite