Dec 11, 2023 · We propose compensation sampling to guide the generation towards the target domain. We introduce a compensation term, implemented as a U-Net, which adds ...

We propose a compensation algorithm for diffusion models that not only boosts the convergence during training without breaking any of the assumptions, but also ...

We apply compensation sampling and achieve results that are on par with, and typically outperform, current state-of-the- art diffusion models on unconditional ...

Finally, we demonstrate the merits of our compensation sampling approach beyond tasks related to face reconstruction, and present additional experiments on ...

Diffusion models achieve remarkable quality in image generation, but at a cost. Iterative denoising requires many time steps to produce high fidelity images ...

This is the codebase for our paper Compensation Sampling for Improved Convergence in Diffusion Models. The repository is based on DDIM tuned by ADM.

Duration: 5:08
Posted: Sep 12, 2024

This approach is designed to improve the efficiency and accuracy of the reverse generation process, enabling better convergence properties compared to existing ...

1.38. Compensation Sampling for Improved Convergence in Diffusion Models. 2023. 4. Diffusion StyleGAN2. 1.69. Diffusion-GAN: Training GANs with Diffusion. 2022.

Missing: Compensation | Show results with:Compensation