Brimkern image pipelines — BRIK (int8 / mixed / int4)
Text→image weights converted to the
BRIK format so diffusion runs
inside the browser on the
visitor's GPU (WebGPU) — no inference server, no image leaving the machine. Two pipelines, sharing one
CLIP text encoder and the
TAESD decoder.
SD-Turbo (default on desktop)
| file | size | role |
|---|
sd-turbo-unet-q8.brik | 921 MB | UNet, int8 |
sd-turbo-unet-mixed.brik | 789 MB | UNet, mixed int8+int4 |
sd-turbo-clip-q8.brik | 362 MB | CLIP text encoder, int8 |
Full pipeline as loaded by the app: 1.29 GB (UNet q8 + CLIP q8 + 4.7 MB TAESD decoder).
SDXS-512 (default on mobile)
| file | size | role |
|---|
sdxs-unet-light.brik | 205 MB | UNet, int4 |
sdxs-unet-mixed.brik | 281 MB | UNet, mixed |
sdxs-unet-q8.brik | 349 MB | UNet, int8 |
sd-turbo-clip-mixed.brik | 235 MB | CLIP text encoder, mixed |
Full pipeline as loaded by the app: 446 MB — a one-step image generator that fits a phone's
budget.
Try it
👉
https://brimkern.com/chat →
Browse / load a model → the image card.
These are pipelines, not single-file models: several files are loaded together, so the
?model=repo deeplink (which picks one file) does not apply here. The app resolves the right set for
the device, streams every file by HTTP Range, caches them, and can then generate offline.
Measured
Chrome, Apple Silicon laptop, production build, SD-Turbo q8 at 256 px, 1 step — replayable benches in
scripts/e2e/
(
profile-image.mjs,
bench-image.mjs):
| before | after | gain |
|---|
| 3×3 int8 convolution (94 shots) | 35 411 µs/shot | 19 217 | ×1.84 |
| GPU total for one generation | 6 455 ms | 3 856 ms | ×1.67 |
| end-to-end 256 px generation | 5.0 s | 3.0 s | ×1.67 |
The gain came from tiling the quantized convolution: the f32 path had a tiled 3×3 kernel from the
start, the int8/int4 paths did not — and int8 is what production runs, since these BRIKs are
pre-quantized. A weight dequantized once per tile instead of 256 times is where the time went.
512 px is the default on a capable machine: SD-Turbo is trained at 512 and below it stops composing
(at 256 the same prompt returns a cropped close-up where 512 returns the whole portrait, compared at
equal seed).
Format
A
.brik is a self-describing container: topology, quantization tiers and configuration travel
inside the file (the SDXS UNet's 3-level, no-mid, fixed-heads config included), and every shard is
one contiguous HTTP range. Specification:
BRIK_FORMAT.md.
License — read this one
These are derived weights, and each source keeps its own terms:
- SD-Turbo (stabilityai/sd-turbo) is released as a
research artifact. Its card states: "For commercial use, please refer to
https://stability.ai/license." Treat the
sd-turbo-* files as non-commercial / research unless
you hold a Stability license.
- SDXS-512 (IDKiro/sdxs-512-0.9) is openrail++.
- The TAESD decoder (madebyollin/taesd) is MIT and is
fetched from its own repository, not mirrored here.
The Brimkern engine itself is MIT. Converting weights does not change their license.
