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# Pixel Diffusion

4 related articles

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  1. arXiv

    Pixel diffusion drops attention mixing: Volterra layers beat JiT's FID at every ImageNet scale

    Pixel-space diffusion models had inherited softmax attention from sequence modeling as their token mixer, although the optimal denoiser under Gaussian noise admits a polynomial functional expansion in the observation. PoD compresses each image patch through a low-dimensional bottleneck and stacks cascaded low-rank Volterra layers that mix tokens and channels, with no activation function on the residual path. At matched parameters and FLOPs on ImageNet, PoD beats JiT in FID at every scale, with the largest gap at the G scale.

    Siddharth Roheda（First author）
    PoDVolterra NetworksPixel DiffusionWiener–Volterra SeriesSelf-Attention
  2. arXiv

    Pixel-space generators stop letting detail inflate global attention: low-frequency carriers cut ImageNet-512 FID by 2.84

    Pixel-space generators feed the full image to a Transformer as patches, so finer detail lengthens the global sequence and raises quadratic attention cost, tying detail to communication. SpecSlot expands each spatial anchor into slots, gives every slot a fixed disjoint group of DCT coefficients, and sends only the lowest-frequency slot per anchor into the global stage while all slots mix locally. At ImageNet-512, the W256/K4 configuration lowers FID by 2.84 over the FLOPs-matched JiT control with 3.9% fewer GFLOPs, and trails JiT-B/16 by 0.69 FID at 38.2% lower cost.

    Jiongfan Yang（First author）
    SpecSlotRole-Coupled TokenizationPixel DiffusionImage GenerationText-to-Image Generation
  3. arXiv

    PerF makes pixel-space diffusion Transformers specialize by feature: FID on ImageNet 256 drops from 1.86 to 1.63 with about 3.6% more parameters

    The work introduces heterogeneous refinement, giving different feature groups distinct refinement budgets across depth in pixel-space diffusion Transformers, which spontaneously yields persistent features that encode global structure and active features that encode high-frequency detail; building on this, Persistence Forcing (PerF) and Persistence Guidance (PG) reduce FID on ImageNet 256 from 3.66, 2.36, and 1.86 to 2.81, 1.91, and 1.63 for JiT-B/L/H, and on ImageNet 512 from 1.94 to 1.76 for JiT-H/32.

    Chong Wang（First author）
    Persistence ForcingHeterogeneous RefinementPersistence GuidancePixel DiffusionDiffusion Transformer
  4. arXiv

    Adversarial post-training restores missing high-frequency detail in pixel diffusion, cutting DeCo FID from 33.27 to 28.59

    By keeping the original diffusion or flow-matching objective and adding an adversarial loss to the predicted output at non-high-noise timesteps, this study jointly improves distribution fidelity, coverage, prompt alignment, and perceptual quality on the DeCo and PixelGen pixel backbones, attributing the gain to restored missing natural-image high-frequency power, while the same procedure yields no comparable gain in the tested latent diffusion configurations.

    Xin Lin（First author）
    adversarial trainingPixel DiffusionText-to-Image Synthesis

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