Same prompt, same seed, same checkpoint, on the same machine — the same frame, byte for byte. That deterministic property is the working core. Below is an account of what the engine is designed to do, what is in development, and what it deliberately does not do yet. Try the Open Studio demo.
This matrix is the product design target. Several capabilities are working today — the deterministic core and re-render, 1080p cinematic clips, character continuity and style continuity; the rest is in active development at varying stages and should be read as where the engine is going.
Same prompt · same seed · same checkpoint, on the same machine and a pinned environment → the same frame, byte for byte. Measured across our SDXL, AnimateDiff and Wan render pipelines. Cross-machine reproducibility is not yet validated.
The engine produces 1080p cinematic clips at 24, 25 or 30 fps, with selectable aspect ratios and camera looks. 1080p output works today; 4K and 8K are the next resolution targets.
A character’s face, hair and wardrobe stay stable across cuts, scenes and longer sequences. Character continuity holds in the current pipeline.
A single style spec governs an entire series so it looks consistent end to end. Style continuity is working in the current pipeline.
Planned camera control — drone aerial, dolly, handheld, lock-off, orbit, push-in, pull-back — with controllable speed and easing. In development.
Planned lighting control — magic hour, blue hour, overcast, neon night, hard noon, practical — with directional control and colour temperature. In development.
Several cinematic style families are in progress (photorealistic, watercolor, stained glass, paper-cut, oil paint, pen-and-ink), with a JSON spec for adding more. Not yet a finished library.
Because output is deterministic at a fixed seed and environment, a re-render reproduces the prior frame exactly — useful for fixes and review without reroll randomness.
The pipeline is being designed to attach a cryptographic content-credential manifest to each output, aligned with the C2PA Content Credentials standard. This is not yet shipped.
A post-render watermark recipe (ffmpeg-based) is planned for the BYOC starter pack, with an optional invisible per-render mark for leak tracing.
Planned: scene briefs, prompts and titles carried as multilingual JSON, so localization does not require re-rendering the visual track. In development.
Planned: a hierarchical schema (series → episode → scene → shot → frame) with cross-references and asset versioning. In development.
Planned: an append-only log of every change — actor, timestamp, before- and after-state, reason. In development.
Development is early. The items below describe internal development progress, not externally audited metrics, and the work is ongoing rather than finished.
Generative video is electricity-hungry. The dominant pattern in 2026 is cloud-render-everywhere: every prompt routes to a hyperscale datacenter where an H100 / B200 cluster spins up, generates, then idles or rolls to the next user. Datacenter PUE (power usage effectiveness) is 1.3–1.5 typical — meaning every watt of compute carries 30–50% additional cooling and infrastructure overhead. At scale, this aggregates to nontrivial gigawatt-hours per million minutes generated.
Our architecture changes two variables in that equation:
Rough order-of-magnitude comparison for one minute of finished 1080p cinematic AI video:
That spread — ~5–10× lower kWh per finished minute — is order-of-magnitude only, not a measured certification. The cloud workflow numbers come from publicly available 2024–2026 industry data on datacenter PUE (Uptime Institute Global Data Center Survey 2024 reports median PUE 1.58; Lawrence Berkeley National Laboratory 2024 estimates AI-training-class workload draws 0.4–1.2 kWh per finished minute of generative video at 1080p). The BYOC numbers come from observed throughput on consumer Nvidia hardware running open-source video diffusion models. Actual numbers vary with local grid mix, GPU class, model choice, render-settings discipline, and the cloud provider compared against. We are not selling carbon credits, we have not gone through SBTi or PCAF certification, and we have no third-party assurance. We claim a more efficient architecture and show how the math comes out. The methodology document publishes shortly in our open repo; until then, audit the inputs above and adjust for your context.
If our work helps a studio swap out cloud-render-everywhere for BYOC + deterministic on a single recurring production, the cumulative annual electricity savings can run into multiple megawatt-hours. That is the operational reason we built this architecture, and it is the reason we will not pivot to hosted cloud render until we can do it with energy accounting we can publish honestly.
Generative AI media without provenance is becoming a regulatory question. The EU AI Act, California SB-942 (Content Provenance Act) and China’s synthetic-content labelling rules all move toward the same direction: synthetic media should be able to declare itself. We are designing with that direction in mind.
The planned approach: emit a C2PA-aligned content-credential manifest alongside each render, recording the prompt and seed, the model checkpoint hash, the render timestamp and a pipeline signature, so that tampering is detectable. This feature is in development and is not yet shipped.
Nothing on this page is a statement of regulatory compliance. Whether any output meets a specific legal obligation depends on how you use it and on your jurisdiction — that assessment is yours to make, and you should confirm your own obligations.
Provenance is a planned capability, not a shipped one. We are an AI media tool designing in cryptographic provenance — not a “Web3 company”, and not a compliance product.
For partnership, licensing, press and security inquiries: use the contact form. We read everything. DMCA notices and security disclosures have dedicated topics on the same form.
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