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Found 5 papers, 3 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Towards Fast Stochastic Sampling in Diffusion Generative Models
We propose Splitting Integrators for fast stochastic sampling in pre-trained diffusion models in augmented spaces.
Efficient Integrators for Diffusion Generative Models
We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting Integrators.
A Complete Recipe for Diffusion Generative Models
Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks.
Ranked #17 on
Image Generation
on CIFAR-10
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation.
Ranked #15 on
Image Generation
on CelebA 64x64
