Card: Why we built Agate (architecture study for glyph/symbol generation; adapter and flow-matching testbed)

README.md

@@ -35,13 +35,8 @@ It is Agate's 256 px network made multi-resolution (additions that start as exac
 
 Built by [LogoLabs](https://logolabs.org), which makes AI logo generation.
 
-**Why we built Agate.** Agate is LogoLabs' search for the best architecture for small-scale image generation, as
-groundwork for glyph and symbol generation: we want to generate vector-native fonts to go with the wordmarks we make at
-LogoLabs. We trained a general text-to-image model because general image generation is a good proxy for how an
-architecture performs on those tasks, and it is very benchmark-friendly (GenEval, Qwen-Image-Bench, FID), unlike tasks
-such as SDF generation, which have almost no established benchmarks. Next, we plan to build on it to generate SVGs with
-our vectoriser, Inkvec, and to improve Inkvec's prior with flow matching to help trace the fonts. We decided to release
-Agate to share these architectural efforts.
+**Why we built Agate:** it is the result of an architecture study for glyph and symbol generation; see
+[Why we built Agate](#why-we-built-agate) below.
 
 <p align="center"><img src="assets/hero_samples.png" alt="Twelve Agate Preview 003 samples at 512 px, seed 0" width="100%"></p>
 
@@ -348,6 +343,36 @@ GH200-hours to encode the 512 px latents and saliency maps. The whole project to
 whose images were generated with FLUX.1-dev. The text encoder is fine-tuned from Ettin-68M (MIT); the SD-VAE and
 TAESD decoders (MIT) are downloaded from their own repositories.
 
+## Why we built Agate
+
+**Agate is the result of an architecture study.** LogoLabs generates logos, and our customers increasingly need a
+matching **font**: every glyph, not a single wordmark. That calls for a small, fast model that generates glyph
+representations such as **signed distance fields (SDFs)**, which can then be turned into clean vectors. No existing
+model does this, so there was nothing in that domain to measure a new architecture against.
+
+We therefore ran the study where baselines exist: **general text-to-image generation** on a large open dataset,
+scored with established benchmarks (GenEval, Qwen-Image-Bench, FID) and compared against a diffusion-transformer
+baseline reproduced on the same data. We compared convolutional, transformer and hybrid designs under one evaluation
+protocol; the winner splits the work between a small transformer planner (the "thinker") and a convolutional renderer.
+Agate is the artifact of that study, and its scores (Preview 001: 0.550 on GenEval at 260M parameters, equal to SDXL's
+published score; Preview 003: above SD 1.5 on Qwen-Image-Bench) are evidence that the architecture is sound before we
+apply it to glyphs.
+
+**What we found along the way.** A model this small turned out to be a research tool in its own right:
+
+- **A cheap testbed for adapter architectures.** An Agate fine-tune takes a few GPU-hours, so we could compare adapter
+  designs quickly: rank sweeps, topic-specialist LoRAs, **mixture-of-LoRA experts** with learned routing, and adapters
+  trained with perceptual objectives, at 2-10 GPU-hours per experiment, roughly a tenth of what the same question costs
+  on a multi-billion-parameter base. It is how we learned, for instance, that adapters trained with plain flow loss
+  quietly undo a perceptually trained base.
+- **Flow matching at a practical scale.** Training a complete text-to-image flow model from scratch in about 150
+  GPU-hours lets a small team run many full trainings, not one, and study objectives, samplers, resolution transfer and
+  routing that would otherwise need a far larger budget.
+
+**Next,** we are applying the architecture to glyph and symbol generation for vector-native fonts to go with our
+wordmarks, traced to SVG by our vectoriser, Inkvec, whose prior we will improve with flow matching. We decided to
+release Agate to share these architectural efforts.
+
 ## Acknowledgements
 
 <p align="center"><img src="assets/acknowledgment-banner.png" alt="EuroHPC Joint Undertaking and Arrhenius" width="100%"></p>