PID: Fast and High-Resolution Latent Decoding with Pixel Diffusion
The article introduces PiD, a new Pixel diffusion Decoder that enhances the process of decoding latent representations into high-resolution images. This model improves efficiency by unifying decoding and upsampling into a single generative module, achieving faster and higher-quality results. PiD can decode images at resolutions of 2048×2048 pixels in under one second, significantly outperforming traditional methods.
- ▪PiD replaces the traditional decode-then-super-resolve cascade with a more efficient pixel diffusion approach.
- ▪The model can decode latents of 512×512 images into 2048×2048 pixels in under 1 second on consumer hardware.
- ▪PiD is reported to be up to 5.9 times faster than existing super-resolution pipelines while maintaining better visual fidelity.
2 outlets in our directory ran this story, first to last over 1 hour. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ ComfyUI node for NVIDIA PiD pixel diffusion decoding — r/StableDiffusion
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Story provenance
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Record
| Original publisher | Nvidia |
| Canonical URL | https://research.nvidia.com/labs/sil/projects/pid/ |
| Publication time | Mon, 25 May 2026 15:23:18 +0000 |
| Retrieval time | 2026-05-25T15:37:38.244Z |
| Last seen | 2026-05-25T15:37:38.244Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | QuBy5UTJMxOF · 3 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
PiD:Fast and High-Resolution Latent Decodingwith Pixel Diffusion Yifan Lu Qi Wu Jay Zhangjie Wu Zian Wang Huan Ling Sanja Fidler Xuanchi Ren NVIDIA Read Paper (arXiv) Model Code TL;DR: PiD directly decodes latent representations into high-resolution images, replacing the decode–then–super-resolve cascade while achieving lower latency and higher visual quality. Real Image Latent Generated Image Latent SD3 VAE VAE Decoder PiD DINOv2 RAE Decoder PiD Z-Image VAE Decoder PiD Flux.2 [dev] VAE Decoder PiD Abstract Most practical high-resolution text-to-image systems rely on latent diffusion models, where generation is performed in a compact latent space and a decoder maps latents back to pixels.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Nvidia.
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