SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time

• URL: http://arxiv.org/abs/2407.15507v1
• Date: Mon, 22 Jul 2024 09:44:35 GMT
• Title: SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time
• Authors: Stanislav Frolov, Brian B. Moser, Andreas Dengel,
• Abstract summary: We present a novel approach to generate high-resolution images with generative models. Our method shifts non-overlapping denoising windows over time, ensuring that seams in one timestep are corrected in the next. Our method offers several key benefits, including improved computational efficiency and faster inference times.
• Score: 7.532695984765271
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating high-resolution images with generative models has recently been made widely accessible by leveraging diffusion models pre-trained on large-scale datasets. Various techniques, such as MultiDiffusion and SyncDiffusion, have further pushed image generation beyond training resolutions, i.e., from square images to panorama, by merging multiple overlapping diffusion paths or employing gradient descent to maintain perceptual coherence. However, these methods suffer from significant computational inefficiencies due to generating and averaging numerous predictions, which is required in practice to produce high-quality and seamless images. This work addresses this limitation and presents a novel approach that eliminates the need to generate and average numerous overlapping denoising predictions. Our method shifts non-overlapping denoising windows over time, ensuring that seams in one timestep are corrected in the next. This results in coherent, high-resolution images with fewer overall steps. We demonstrate the effectiveness of our approach through qualitative and quantitative evaluations, comparing it with MultiDiffusion, SyncDiffusion, and StitchDiffusion. Our method offers several key benefits, including improved computational efficiency and faster inference times while producing comparable or better image quality.

Related papers

• Fast constrained sampling in pre-trained diffusion models [77.21486516041391]
  Diffusion models have dominated the field of large, generative image models. We propose an algorithm for fast-constrained sampling in large pre-trained diffusion models.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• A Wavelet Diffusion GAN for Image Super-Resolution [7.986370916847687]
  Diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation. However, their real-time feasibility is hindered by slow training and inference speeds. This study proposes a wavelet-based conditional Diffusion GAN scheme for Single-Image Super-Resolution.
  arXiv  Detail & Related papers  (2024-10-23T15:34:06Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction [4.227116189483428]
  This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation framework. It includes the low-quality image generation in latent space and the high-quality image generation in pixel space. It minimizes computational costs by moving some inference steps from pixel space to latent space.
  arXiv  Detail & Related papers  (2024-03-14T12:58:28Z)
• Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
  This study proposes a novel and efficient diffusion model for image restoration. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
  arXiv  Detail & Related papers  (2024-03-12T05:06:07Z)
• Decoupled Data Consistency with Diffusion Purification for Image Restoration [15.043002968696978]
  We propose a novel diffusion-based image restoration solver that addresses issues by decoupling the reverse process from the data consistency steps. Our approach demonstrates versatility, making it highly adaptable for efficient problem-solving in latent space. The efficacy of our approach is validated through comprehensive experiments across various image restoration tasks, including image denoising, deblurring, inpainting, and super-resolution.
  arXiv  Detail & Related papers  (2024-03-10T00:47:05Z)
• CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation [49.3016007471979]
  Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks. However, their widespread adoption is hindered by the high computational cost, which limits their real-time application. We introduce a novel method dubbed CoDi, that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs.
  arXiv  Detail & Related papers  (2023-10-02T17:59:18Z)
• ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting [70.83632337581034]
  Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed. We propose a novel and efficient diffusion model for SR that significantly reduces the number of diffusion steps. Our method constructs a Markov chain that transfers between the high-resolution image and the low-resolution image by shifting the residual.
  arXiv  Detail & Related papers  (2023-07-23T15:10:02Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)

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.