AnimateDiff-Lightning motion modules on an SD 1.5 base, converted to the BRIK format so text→video runs inside the browser on the visitor's GPU (WebGPU). No inference server, no upload: the prompt and the frames never leave the machine.

Full pipeline as loaded by the app: 1.53 GB (the three files + the 4.7 MB TAESD decoder, fetched from its own repository).

👉 https://brimkern.com/chat → Browse / load a model → the video card.

This is a pipeline, not a single-file model: several files load together, so the ?model=repo deeplink does not apply. Every file is streamed by HTTP Range, cached, and reusable offline.

⚠️ Honest cost, announced before the click: 1.53 GB to download, then several minutes of GPU work for a few seconds of video. Clip length is adjustable from 8 to 32 frames, with the cost shown next to the choice. A progress fraction (UNet blocks per step, then frames decoded) drives a real progress bar and a remaining-time estimate — a diffusion wait with no feedback is indistinguishable from a hang.

The video path shares the SD 1.5 UNet with the image pipeline, so it inherits its measured kernel work: the tiled int8 3×3 convolution (×1.84 on that kernel, ×1.67 end-to-end on a 256 px image). Replayable benches in scripts/e2e/.

No end-to-end seconds-per-clip figure is published here: it depends on frame count, resolution and the GPU duty setting, and the project's rule is that a number gets published only if a replayable bench produced it under stated conditions.

A .brik is a self-describing container: topology and quantization tiers travel inside the file, and every shard is one contiguous HTTP range — which is what makes a 1.53 GB pipeline loadable in a tab. Specification: BRIK_FORMAT.md.

Derived weights; the sources keep their terms:

The Brimkern engine itself is MIT. Converting weights does not change their license.