most citedPRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

2 citations · 2 across the 1 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Imagen 3

Imagen-Team-Google, :, Jason Baldridge +257

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred…

cs.LG2024

EM Distillation for One-step Diffusion Models

Sirui Xie, Zhisheng Xiao, Diederik P Kingma +6

While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, bu…

cs.LG20242 cited

PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

Fei Deng, Qifei Wang, Wei Wei +2

Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using…

cs.CV2023

UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs

Yanwu Xu, Yang Zhao, Zhisheng Xiao +1

Text-to-image diffusion models have demonstrated remarkable capabilities in transforming textual prompts into coherent images, yet the computational cost of their inference remains…

cs.CV2023

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models

Shaoan Xie, Yang Zhao, Zhisheng Xiao +5

This study introduces Text-Guided Subject-Driven Image Inpainting, a novel task that combines text and exemplar images for image inpainting. While both text and exemplar images hav…

cs.CV2023

HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models

Zhonghao Wang, Wei Wei, Yang Zhao +4

This paper explores advancements in high-fidelity personalized image generation through the utilization of pre-trained text-to-image diffusion models. While previous approaches hav…