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Found 17 papers, 12 papers with code
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data
We open-source our code and the trained Ambient Diffusion MRI models: https://github. com/utcsilab/ambient-diffusion-mri .
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models.
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
We present the first diffusion-based framework that can learn an unknown distribution using only highly-corrupted samples.
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers
We develop a framework for non-asymptotic analysis of deterministic samplers used for diffusion generative modeling.
Multiresolution Textual Inversion
We extend Textual Inversion to learn pseudo-words that represent a concept at different resolutions.
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
We find a surprising connection between multitask learning and robustness to neuron failures.
Soft Diffusion: Score Matching for General Corruptions
To reverse these general diffusions, we propose a new objective called Soft Score Matching that provably learns the score function for any linear corruption process and yields state of the art results for CelebA.
Ranked #7 on
Image Generation
on CelebA 64x64
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems
In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model.
Discovering the Hidden Vocabulary of DALLE-2
We discover that DALLE-2 seems to have a hidden vocabulary that can be used to generate images with absurd prompts.
Solving Inverse Problems with NerfGANs
We introduce a novel framework for solving inverse problems using NeRF-style generative models.
Robust Compressed Sensing MR Imaging with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Robust Compressed Sensing MRI with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models.
SMYRF - Efficient Attention using Asymmetric Clustering
We propose a novel type of balanced clustering algorithm to approximate attention.
SMYRF: Efficient Attention using Asymmetric Clustering
We also show that SMYRF can be used interchangeably with dense attention before and after training.
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality.
Ranked #19 on
Conditional Image Generation
on ImageNet 128x128
