Model-Agnostic Human Preference Inversion in Diffusion Models

• URL: http://arxiv.org/abs/2404.00879v1
• Date: Mon, 1 Apr 2024 03:18:12 GMT
• Title: Model-Agnostic Human Preference Inversion in Diffusion Models
• Authors: Jeeyung Kim, Ze Wang, Qiang Qiu,
• Abstract summary: We propose a novel sampling design to achieve high-quality one-step image generation aligning with human preferences. Our approach, Prompt Adaptive Human Preference Inversion (PAHI), optimize the noise distributions for each prompt based on human preferences. Our experiments showcase that the tailored noise distributions significantly improve image quality with only a marginal increase in computational cost.
• Score: 31.992947353231564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient text-to-image generation remains a challenging task due to the high computational costs associated with the multi-step sampling in diffusion models. Although distillation of pre-trained diffusion models has been successful in reducing sampling steps, low-step image generation often falls short in terms of quality. In this study, we propose a novel sampling design to achieve high-quality one-step image generation aligning with human preferences, particularly focusing on exploring the impact of the prior noise distribution. Our approach, Prompt Adaptive Human Preference Inversion (PAHI), optimizes the noise distributions for each prompt based on human preferences without the need for fine-tuning diffusion models. Our experiments showcase that the tailored noise distributions significantly improve image quality with only a marginal increase in computational cost. Our findings underscore the importance of noise optimization and pave the way for efficient and high-quality text-to-image synthesis.

Related papers

• Arbitrary-steps Image Super-resolution via Diffusion Inversion [68.78628844966019]
  This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point. Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result.
  arXiv  Detail & Related papers  (2024-12-12T07:24:13Z)
• Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization [97.35427957922714]
  We present an algorithm named pairwise sample optimization (PSO), which enables the direct fine-tuning of an arbitrary timestep-distilled diffusion model. PSO introduces additional reference images sampled from the current time-step distilled model, and increases the relative likelihood margin between the training images and reference images. We show that PSO can directly adapt distilled models to human-preferred generation with both offline and online-generated pairwise preference image data.
  arXiv  Detail & Related papers  (2024-10-04T07:05:16Z)
• Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis [22.02829139522153]
  We propose an efficient time step sampling method based on an image spectral analysis of the diffusion process. Instead of the traditional uniform distribution-based time step sampling, we introduce a Beta distribution-like sampling technique. Our hypothesis is that certain steps exhibit significant changes in image content, while others contribute minimally.
  arXiv  Detail & Related papers  (2024-07-16T20:53:06Z)
• Improved Noise Schedule for Diffusion Training [51.849746576387375]
  We propose a novel approach to design the noise schedule for enhancing the training of diffusion models. We empirically demonstrate the superiority of our noise schedule over the standard cosine schedule.
  arXiv  Detail & Related papers  (2024-07-03T17:34:55Z)
• Diffusion Posterior Proximal Sampling for Image Restoration [27.35952624032734]
  We present a refined paradigm for diffusion-based image restoration. Specifically, we opt for a sample consistent with the measurement identity at each generative step. The number of candidate samples used for selection is adaptively determined based on the signal-to-noise ratio of the timestep.
  arXiv  Detail & Related papers  (2024-02-25T04:24:28Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)
• Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
  We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation. Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping. We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
  arXiv  Detail & Related papers  (2023-06-23T18:08:00Z)
• Accelerating Score-based Generative Models for High-Resolution Image Synthesis [42.076244561541706]
  Score-based generative models (SGMs) have recently emerged as a promising class of generative models. In this work, we consider the acceleration of high-resolution generation with SGMs. We introduce a novel Target Distribution Sampling Aware (TDAS) method by leveraging the structural priors in space and frequency domains.
  arXiv  Detail & Related papers  (2022-06-08T17:41:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.