Summary of Distilling Diffusion Models Into Conditional Gans, by Minguk Kang et al.
Distilling Diffusion Models into Conditional GANs
by Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun-Yan Zhu, Taesung Park
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method distills a complex multistep diffusion model into a single-step conditional GAN student model, significantly accelerating inference while preserving image quality. The approach interprets diffusion distillation as a paired image-to-image translation task using noise-to-image pairs of the ODE trajectory. To efficiently compute regression loss, E-LatentLPIPS is proposed, operating directly in the diffusion model’s latent space with an ensemble of augmentations. Additionally, a diffusion model is adapted to construct a multi-scale discriminator with text alignment loss for effective conditional GAN-based formulation. The one-step generator outperforms existing models like DMD, SDXL-Turbo, and SDXL-Lightning on the zero-shot COCO benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us make pictures faster without losing quality. It takes a complex way of making images and simplifies it into a single step, making it much quicker to generate images. This is done by looking at how the original image-making process works and using that information to create a new, simpler process. The method also makes sure that the generated images look similar to real ones, which is important for tasks like image recognition. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Diffusion model » Distillation » Gan » Inference » Latent space » Regression » Student model » Translation » Zero shot
