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Found 16 papers, 8 papers with code
TryOnDiffusion: A Tale of Two UNets
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person.
Synthetic Data from Diffusion Models Improves ImageNet Classification
Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts.
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.
Character-Aware Models Improve Visual Text Rendering
In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell).
Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting
Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.
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
Re-Imagen: Retrieval-Augmented Text-to-Image Generator
To further evaluate the capabilities of the model, we introduce EntityDrawBench, a new benchmark that evaluates image generation for diverse entities, from frequent to rare, across multiple object categories including dogs, foods, landmarks, birds, and characters.
Ranked #3 on
Text-to-Image Generation
on MS COCO
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 #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Deblurring via Stochastic Refinement
Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input.
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
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 #3 on
Image Generation
on ImageNet 64x64
Image Super-Resolution via Iterative Refinement
We present SR3, an approach to image Super-Resolution via Repeated Refinement.
Non-Autoregressive Machine Translation with Latent Alignments
In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline.
Imputer: Sequence Modelling via Imputation and Dynamic Programming
This paper presents the Imputer, a neural sequence model that generates output sequences iteratively via imputations.
Combating False Negatives in Adversarial Imitation Learning
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior.
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts.
