Generalized Consistency Trajectory Models for Image Manipulation

• URL: http://arxiv.org/abs/2403.12510v3
• Date: Thu, 10 Oct 2024 04:04:44 GMT
• Title: Generalized Consistency Trajectory Models for Image Manipulation
• Authors: Beomsu Kim, Jaemin Kim, Jeongsol Kim, Jong Chul Ye,
• Abstract summary: Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. This work aims to unlock the full potential of consistency trajectory models (CTMs) by proposing generalized CTMs (GCTMs) We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.
• Score: 59.576781858809355
• License:
• Abstract: Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. The success of DMs lies in the iterative nature of diffusion: diffusion breaks down the complex process of mapping noise to data into a sequence of simple denoising tasks. Moreover, we are able to exert fine-grained control over the generation process by injecting guidance terms into each denoising step. However, the iterative process is also computationally intensive, often taking from tens up to thousands of function evaluations. Although consistency trajectory models (CTMs) enable traversal between any time points along the probability flow ODE (PFODE) and score inference with a single function evaluation, CTMs only allow translation from Gaussian noise to data. This work aims to unlock the full potential of CTMs by proposing generalized CTMs (GCTMs), which translate between arbitrary distributions via ODEs. We discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing.

Related papers

• Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations [5.517338199249029]
  SR-DDPM is a class of generative models that produce high-quality images by reversing a noisy diffusion process. By exploiting the similarity between diverse data distributions, our method can scale to multiple tasks without compromising the image quality. We evaluate our method on standard image datasets and show that it outperforms both unconditional and conditional DDPM in terms of FID and SSIM metrics.
  arXiv  Detail & Related papers  (2023-11-27T22:30:26Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Markup-to-Image Diffusion Models with Scheduled Sampling [111.30188533324954]
  Building on recent advances in image generation, we present a data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations. We conduct experiments on four markup datasets: mathematical formulas (La), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES)
  arXiv  Detail & Related papers  (2022-10-11T04:56:12Z)
• Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models [54.1843419649895]
  A major challenge in using generative models to accomplish this task is the lack of paired data containing all modalities and corresponding outputs. We propose a solution based on a denoising diffusion probabilistic synthesis models to generate images under multi-model priors.
  arXiv  Detail & Related papers  (2022-06-10T12:23:05Z)
• The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models [0.8999666725996974]
  We build on a method for image-to-image translation using denoising diffusion implicit models. We apply our method to simulate the aging process on facial photos using a regression task. We also use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images.
  arXiv  Detail & Related papers  (2022-04-06T07:52:06Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)
• Score-based diffusion models for accelerated MRI [35.3148116010546]
  We introduce a way to sample data from a conditional distribution given the measurements, such that the model can be readily used for solving inverse problems in imaging. Our model requires magnitude images only for training, and yet is able to reconstruct complex-valued data, and even extends to parallel imaging.
  arXiv  Detail & Related papers  (2021-10-08T08:42:03Z)

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.