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Found 12 papers, 9 papers with code
Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation
Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expensive and difficult optimization due to its reliance on a fixed pretrained DINOv2 discriminator.
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Rectified flow is a recent generative model formulation that connects data and noise in a straight line.
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation.
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
We present SDXL, a latent diffusion model for text-to-image synthesis.
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i. e., videos.
Ranked #5 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
Latent Space Diffusion Models of Cryo-EM Structures
The state-of-the-art method cryoDRGN uses a Variational Autoencoder (VAE) framework to learn a continuous distribution of protein structures from single particle cryo-EM imaging data.
Differentially Private Diffusion Models
While modern machine learning models rely on increasingly large training datasets, data is often limited in privacy-sensitive domains.
GENIE: Higher-Order Denoising Diffusion Solvers
Synthesis amounts to solving a differential equation (DE) defined by the learnt model.
Ranked #5 on
Image Generation
on AFHQV2
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise.
Ranked #24 on
Image Generation
on CIFAR-10
Demystifying and Generalizing BinaryConnect
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization.
Density Deconvolution with Normalizing Flows
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples.
A Discussion on Solving Partial Differential Equations using Neural Networks
Can neural networks learn to solve partial differential equations (PDEs)?
