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Found 8 papers, 1 papers with code
Video Interpolation with Diffusion Models
We present VIDIM, a generative model for video interpolation, which creates short videos given a start and end frame.
ReconFusion: 3D Reconstruction with Diffusion Priors
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks.
Novel View Synthesis with Diffusion Models
We demonstrate that stochastic conditioning significantly improves the 3D consistency of a naive sampler for an image-to-image diffusion model, which involves conditioning on a single fixed view.
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
We introduce Differentiable Diffusion Sampler Search (DDSS): a method that optimizes fast samplers for any pre-trained diffusion model by differentiating through sample quality scores.
Optimizing Few-Step Diffusion Samplers by Gradient Descent
We propose Generalized Gaussian Diffusion Processes (GGDP), a family of non-Markovian samplers for diffusion models, and we show how to improve the generated samples of pre-trained DDPMs by optimizing the degrees of freedom of the GGDP sampler family with respect to a perceptual loss.
Learning to Efficiently Sample from Diffusion Probabilistic Models
Key advantages of DDPMs include ease of training, in contrast to generative adversarial networks, and speed of generation, in contrast to autoregressive models.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.
