Search Results for author:
Found 31 papers, 19 papers with code
On Distillation of Guided Diffusion Models
For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet 64x64 and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from.
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.
Imagen Video: High Definition Video Generation with Diffusion Models
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models.
Ranked #1 on
Video Generation
on LAION-400M
Classifier-Free Diffusion Guidance
Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #16 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Video Diffusion Models
Generating temporally coherent high fidelity video is an important milestone in generative modeling research.
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.
Progressive Distillation for Fast Sampling of Diffusion Models
Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps.
Ranked #45 on
Image Generation
on CIFAR-10
On Density Estimation with Diffusion Models
In addition, we show that the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints.
Palette: Image-to-Image Diffusion Models
We expect this standardized evaluation protocol to play a role in advancing image-to-image translation research.
Ranked #1 on
Colorization
on ImageNet ctest10k
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.
Unconditional Diffusion Guidance
Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models.
Structured Denoising Diffusion Models in Discrete State-Spaces
Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities.
Variational Diffusion Models
In addition, we show that the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints.
Ranked #2 on
Density Estimation
on CIFAR-10
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.
Cascaded Diffusion Models for High Fidelity Image Generation
We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality.
Ranked #8 on
Image Generation
on ImageNet 64x64
Image Super-Resolution via Iterative Refinement
We present SR3, an approach to image Super-Resolution via Repeated Refinement.
Importance weighted compression
The connection between variational autoencoders (VAEs) and compression is well established and they have been used for both lossless and lossy compression.
Should EBMs model the energy or the score?
Recent progress in training unnormalized models through denoising score matching with Langevin dynamics (SMLD) and denoising diffusion probabilistic modeling (DDPM) has made unnormalized models a competitive model class for generative modeling.
Denoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Ranked #2 on
Image Generation
on LSUN Bedroom
Axial Attention in Multidimensional Transformers
We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors.
Ranked #29 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
Natural Image Manipulation for Autoregressive Models Using Fisher Scores
Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim.
Compression with Flows via Local Bits-Back Coding
Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood.
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
The bits-back argument suggests that latent variable models can be turned into lossless compression schemes.
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Ranked #17 on
Image Generation
on ImageNet 32x32
(bpd metric)
Evolved Policy Gradients
We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms.
Meta Learning Shared Hierarchies
We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.
One-Shot Imitation Learning
A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Generative Adversarial Imitation Learning
Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal.
Model-Free Imitation Learning with Policy Optimization
In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations.
