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Generative Synchronous Diffusion

The fourth pillar of generative AI. Every layer of a video, in one pass.

A glowing human brain retrieves memories, locks one blueprint around itself and springs out six layers in one pass: video, voice, lip sync, effects, music and text

Four Pillars

Four Pillars. GSD Is the Fourth.

Each one changed what AI could make. GSD changes the complexity class.

01

Pillar One · Language

Attention

Θ(N²)per layer

Gave us language models.

TextQuadratic cost
02

Pillar Two · Clips

Diffusion Transformer

Θ(L·M²·N²)

Gave us today’s video models.

8 to 30 s clips
03

Pillar Three · Streams

State Space Model

O(N)

Gave us long streams.

Drifts after 10 to 15 s
04

Pillar Four · Wishtales GSD

Topological Graph World Model

O(M log N + N)

The whole video, every layer, in one pass.

120 s+Six layersOne pass

M: synchronised modalities. N: sequence length. L: denoising steps.

How GSD Works

Plan Once. Render Once.

GSD understands the whole story first, then executes it.

One passOne brief
  • Video
  • Voice
  • Lip sync
  • Effects
  • Music
  • Text
End-to-end, one pass01O(M log N)+02O(1)+03O(N)=O(M log N + N)
  1. Symbolic Layer

    Retrieve

    O(M log N)

    Finds every character, voice and sound the story needs in the world graph.

  2. Combinatorial Optimisation

    Optimise

    O(1) amortised

    Locks one quasi-deterministic blueprint. Every sync decision, made once.

  3. Neural Layer

    Render

    O(N)

    Six layers from one shared state, in a single linear pass.

“Synchronisation is a property of the architecture, not a correction applied to the output.”

120 s+

One shot, 154 s demonstrated

≈240 s

To render 120 seconds of six-layer 4K video

≈67×

Realised inference efficiency

Zero

Denoising steps

Measured on NVIDIA Blackwell B300.

Topological Identity

Where Shape Replaces Text Embeddings.

One Topological Graph Signature holds identity across every modality, every scene and every edit.

A doughnut turns into a coffee mug, then a kettlebell, then back into a doughnut, holding one topological graph signature, one piece and one hole, across image, text, voice and music

98.99%

Prompt to physics adherence

99.89%

Prompt constraint adherence

Across 10,000 outputs, measured on NVIDIA Blackwell B300.

Giga Papaya

Editing to the Second.

Only the chosen layer re-renders for the chosen seconds.

Benchmarks

Five Numbers That Define the Gap.

Version 2026 evaluation against the commercial frontier.

29 of 39

Benchmark categories led

MeasureWishtales GSDLeading video modelsGap
Single pass, no cuts120 s+, 154 s demonstrated8 to 30 s4× or more
Native quality4K at 50 fps, whole pass720p to 4K, 24 to 60 fps, for 8 to 30 sSame fidelity, many times the length
Speed, output s per wall-clock s0.5, six layers0.03 to 0.09, one layer5× to 17×
Price per minute with audioUSD 2, six layers, ten editsUSD 24 to 4212× to 21×
Editable layers6, to one second2: video and one mixed trackDifferent in kind

Wishtales measured on NVIDIA Blackwell B300 at FP8. Leading video models: Seedance, Veo, Kling, Runway and Sora, from public documentation, hosted medians and list pricing for premium tiers with native audio.

Responsible by Design

Safety, Governance and Ethics, Built In.

The rules live inside the world graph, not in a filter after the fact.

01

Safe by Construction

Harmful concepts are left out of the world graph, so they cannot be generated.

Safety in the graph
02

Governed for Enterprise

Brand rules and prohibited concepts are enforced inside the graph, as the video is made.

Brand rules enforced
03

Ethical by Default

No deepfakes. What is left out of the world graph cannot be made, so misuse stops before a single frame exists.

No deepfakes
04

Traceable

Every creative decision is reproducible and reversible, from one quasi-deterministic blueprint.

Version control

See What One Shot Can Do.