Mitigating Exposure Bias in Discriminator Guided Diffusion Models
This improves image generation quality for diffusion model users, though it appears incremental as it builds on existing guidance techniques.
The paper addresses exposure bias in discriminator-guided diffusion models, showing that existing methods don't fully solve this problem, and proposes SEDM-G++ which combines discriminator guidance with epsilon scaling to achieve a state-of-the-art FID score of 1.73 on CIFAR-10.
Diffusion Models have demonstrated remarkable performance in image generation. However, their demanding computational requirements for training have prompted ongoing efforts to enhance the quality of generated images through modifications in the sampling process. A recent approach, known as Discriminator Guidance, seeks to bridge the gap between the model score and the data score by incorporating an auxiliary term, derived from a discriminator network. We show that despite significantly improving sample quality, this technique has not resolved the persistent issue of Exposure Bias and we propose SEDM-G++, which incorporates a modified sampling approach, combining Discriminator Guidance and Epsilon Scaling. Our proposed approach outperforms the current state-of-the-art, by achieving an FID score of 1.73 on the unconditional CIFAR-10 dataset.