An unofficial distillation of LogoLabs' Agate Preview 002 (260M parameters, MIT) into a 4-step model with classifier-free guidance baked in: 4 network passes per image instead of the teacher's 100 (50 Euler steps × a conditional and an unconditional pass). Same architecture, same 256 px output, same agate/ code; only the weights differ. in your browser (WebGPU, nothing leaves your machine) · Use steps=4, cfg=1.0. The bundled pipeline skips the unconditional pass when cfg == 1.0. Every image carries Agate's invisible watermark (payload AGATE002) and provenance metadata; pass watermark=False only for images that go into metrics. A WebGPU build (webgpu/, same graph I/O as Agate's own) runs in…