Scale Space Diffusion

• URL: http://arxiv.org/abs/2603.08709v1
• Date: Mon, 09 Mar 2026 17:59:42 GMT
• Title: Scale Space Diffusion
• Abstract summary: Scale-space theory exhibits a similar hierarchy via low-pass filtering.<n>We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images.<n>We introduce Flexi-UNet, a UNet variant that performs resolution-preserving and resolution-increasing denoising using only the necessary parts of the network.
• Score: 46.993305407761675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images - raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the diffusion process by formulating a family of diffusion models with generalized linear degradations and practical implementations. Using downsampling as the degradation yields our proposed Scale Space Diffusion. To support Scale Space Diffusion, we introduce Flexi-UNet, a UNet variant that performs resolution-preserving and resolution-increasing denoising using only the necessary parts of the network. We evaluate our framework on CelebA and ImageNet and analyze its scaling behavior across resolutions and network depths. Our project website ( https://prateksha.github.io/projects/scale-space-diffusion/ ) is available publicly.

Related papers

• PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution [95.61422489193113]
  We develop a lightweight diffusion method, PGP-DiffSR, to remove redundant information from diffusion models.<n>Our method achieves competitive restoration quality while significantly reducing computational load and memory consumption.
  arXiv  Detail & Related papers  (2025-12-02T12:06:39Z)
• Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models [82.8261101680427]
  Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks, including image inversion, inversion, and editing. We propose Smooth Diffusion, a new category of diffusion models that can be simultaneously high-performing and smooth.
  arXiv  Detail & Related papers  (2023-12-07T16:26:23Z)
• ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation [15.057394130958583]
  ElasticDiffusion is a training-free decoding method that enables pretrained text-to-image diffusion models to generate images with various sizes.<n>Our experiments and qualitative results show superior image coherence quality across aspect ratios compared to MultiDiffusion and the standard decoding strategy of Stable Diffusion.
  arXiv  Detail & Related papers  (2023-11-30T18:58:17Z)
• Diffusion Models Without Attention [110.5623058129782]
  Diffusion State Space Model (DiffuSSM) is an architecture that supplants attention mechanisms with a more scalable state space model backbone. Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward.
  arXiv  Detail & Related papers  (2023-11-30T05:15:35Z)
• Simple diffusion: End-to-end diffusion for high resolution images [27.47227724865238]
  This paper aims to improve denoising diffusion for high resolution images while keeping the model as simple as possible. The four main findings are: 1) the noise schedule should be adjusted for high resolution images, 2) It is sufficient to scale only a particular part of the architecture, 3) dropout should be added at specific locations in the architecture, and 4) downsampling is an effective strategy to avoid high resolution feature maps.
  arXiv  Detail & Related papers  (2023-01-26T13:35:02Z)
• SDM: Spatial Diffusion Model for Large Hole Image Inpainting [106.90795513361498]
  We present a novel spatial diffusion model (SDM) that uses a few iterations to gradually deliver informative pixels to the entire image. Also, thanks to the proposed decoupled probabilistic modeling and spatial diffusion scheme, our method achieves high-quality large-hole completion.
  arXiv  Detail & Related papers  (2022-12-06T13:30:18Z)
• Dimensionality-Varying Diffusion Process [52.52681373641533]
  Diffusion models learn to reverse a signal destruction process to generate new data. We make a theoretical generalization of the forward diffusion process via signal decomposition. We show that our strategy facilitates high-resolution image synthesis and improves FID of diffusion model trained on FFHQ at $1024times1024$ resolution from 52.40 to 10.46.
  arXiv  Detail & Related papers  (2022-11-29T09:05:55Z)
• Subspace Diffusion Generative Models [4.310834990284412]
  Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities.
  arXiv  Detail & Related papers  (2022-05-03T13:43:47Z)

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.