The bet
Every latent line in this atlas shares one premise: that raw pixel space has too many spatially-correlated dimensions to model directly, so a separately trained encoder must first project images onto a lower-dimensional manifold. This line denies it. The claim is that the denoising or flow-matching objective already forces the network to discover that manifold, and that large patches plus the right prediction target let a single end-to-end model do the compression internally — with no VAE, no cascade, and no second training stage.
The payoff, stated in almost the same words across PixNerd, JiT, DiP and HyperDiT: you lose the tokenizer’s reconstruction ceiling, the blur and colour artifacts it imposes, and the non-end-to-end pipeline it requires.
The arc
PixNerd is the technical origin, in July 2025, replacing the VAE with a per-patch neural field and reaching ImageNet-256 FID 2.15 in one stage. Four months later JiT makes the argument the field actually heard — that ε-prediction, not pixels, caused pixel-space instability, and that x-prediction on large patches makes a plain ViT competitive. JiT is usually cited as where this line begins. It is not, and the distinction matters for anyone tracing priority.
What follows splits into three kinds of work. Architecture: PixelDiT and DiP, both at CVPR 2026, separate global semantics from texture refinement across two levels — a pattern HyperDiT extends with semantic anchors and foundation-model register tokens. Diagnosis: PixelREPA finds that REPA, which reliably accelerates latent diffusion transformers, actively hurts JiT; Register Guidance finds register tokens help pixel-space transformers more than latent ones. Both say the same thing from different directions — pixel space is not latent space with more dimensions, and interventions do not transfer between them. Efficiency: MOSAIK cuts 70% of FLOPs with content-aware patch sizing, and the empirical text-to-image study reports 3.2–4.8× faster inference than latent baselines, inverting the efficiency argument that motivated latents in the first place.
Parallel Rollout Approximation is the line’s most useful piece of evidence, because it is not diffusion at all. If the tokenizer is genuinely dispensable, that should not depend on the generative objective — and a pixel-space autoregressive model at 511M parameters reaching FID 1.94 suggests it does not.
Why the FID numbers here cannot be ranked
The papers on this page report ImageNet-256 FIDs of 1.51, 1.52, 1.56, 1.58, 1.61, 1.79, 1.82, 1.94 and 2.15. That spread looks like a leaderboard and is not one. The entries differ in parameter count (511M to 952M), training budget (160 to 600 epochs), guidance scale (2.75 to 3.5) and guidance interval, and every one of those moves FID by more than the gaps between neighbouring rows. The guidance notebook shows a single frozen model moving further under sampler changes alone than these architectures move from each other. Read the within-paper ablations instead; see why the numbers cannot rank the approaches.
Two further cautions specific to this line. The two lowest numbers, from PixSGR and Observation Operators, are single-version preprints, and Observation Operators shares four authors with HyperDiT, so it is one group’s continuing programme rather than independent confirmation.
What holds up, and what does not
The reconstruction-ceiling argument holds: no paper here has been refuted, and removing a lossy component removes its failure modes. The efficiency objection that justified latents is now contested in the right direction, by measurement rather than assertion.
What does not yet hold is the scope. Nearly every headline number on this page is class-conditional ImageNet. Text-to-image evidence is one systematic study old. And the line’s own strongest paper is candid about the gap: PixelDiT states plainly that pixel data and noise distributions are more complex than latent ones and that no mature training recipe exists for them. Attention cost still scales badly with resolution — several papers here exist only to patch that, which is itself a measure of how binding the constraint remains.
A correction this atlas owes the reader
Until September 2026 this line held two papers, both from MIT, and its own text told readers that pixel-space results were “mostly from one group”. That was not a fact about the field. It was this atlas’s coverage gap stated as one. The verified affiliations now on these pages span NVIDIA, Google DeepMind, Yandex Research, Alibaba, Tencent Youtu, KAIST, NTU Singapore, UESTC, Nanjing University, NUS, Peking University, HKU, Stability AI, Stanford, Caltech and ByteDance Seed.
Anyone reading this line should also read cascaded and multiscale pixel diffusion, which made a version of the same argument years earlier and was largely ignored. SiD2 reached FID 1.48 on ImageNet 512 in pixel space in 2024 — still unbeaten at that resolution by anything on this page.