Advanced Pixel Diffusion Model with Guided Sparse Global Refinement

• URL: http://arxiv.org/abs/2609.00798v1
• Date: Tue, 01 Sep 2026 06:50:37 GMT
• Title: Advanced Pixel Diffusion Model with Guided Sparse Global Refinement
• Abstract summary: We propose PixSGR, a novel Pixel diffusion framework with Sparse Global Refinement tailored for modeling the distribution of natural images directly in pixel space.<n>PixSGR starts from a supervised low-channel bottleneck to efficiently capture the low-dimensional manifold of natural images.<n>It then progressively expands the channel dimensionality and spatial resolution to recover increasingly fine-grained structures.
• Score: 22.761844947334847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixel domain. However, pixel-space diffusion is computationally demanding due to the extremely high dimensionality of natural images. For efficiency, existing pixel diffusion models either compromise fine details with large-patch tokenization or confine subsequent refinement within individual patches. Such intra-patch refinement inevitably restricts structural continuity across patch boundaries and long-range token interactions, limiting refinement quality. To address these issues, we propose PixSGR, a novel Pixel diffusion framework with Sparse Global Refinement tailored for modeling the distribution of natural images directly in pixel space. PixSGR starts from a supervised low-channel bottleneck to efficiently capture the low-dimensional manifold of natural images. It then progressively expands the channel dimensionality and spatial resolution to recover increasingly fine-grained structures. At the spatial refinement stage, coarse-scale attention maps preselect globally relevant interactions to pre-sparsify fine-scale attention, enabling non-local refinement beyond isolated patches without the quadratic cost of dense attention. Extensive experiments on ImageNet validate the effectiveness of PixSGR. It achieves an FID of 1.51 at 256$\times$256 and maintains performance when scaled to 512$\times$512, attaining an FID of 1.60.

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