Why Agate: general image generation as a benchmark-friendly proxy

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018EtDckYfNgMbkwqVmTsdha

README.md

@@ -25,13 +25,13 @@ on your own GPU through WebGPU (onnxruntime-web).
 
 **Runs entirely in your browser — prompts never leave your machine.** There is no server and no API call.
 
-**Why we built Agate.** Agate is LogoLabs' search for the best architecture for small-scale image generation. It is
-not meant as a general image model: it is the groundwork for glyph and symbol generation, so that we can generate
-vector-native fonts to go with the wordmarks we make at LogoLabs. Font and SVG generation have almost no established
-benchmarks, so we tested the architecture on natural images, where GenEval, Qwen-Image-Bench and FID exist, as a proxy
-for architectural performance. 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.** 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.
 
 ## Models
 

index.html

@@ -133,7 +133,7 @@
     <p class="small-print">Every image made here is marked as AI-generated (EU AI Act Art. 50): an invisible watermark in the
       pixels (invisible-watermark “dwtDctSvd”, payload AGATE + release) and, in the saved PNG, text fields
       <code>ai_generated</code>, <code>generator</code>, <code>model</code>. Neither survives every edit; please label AI images you publish.</p>
-    <p class="small-print">Why Agate: LogoLabs' search for the best small-scale image architecture, the groundwork for glyph and symbol generation and vector-native fonts to go with our wordmarks. Natural images served as a benchmarkable proxy; next it feeds SVG generation with Inkvec. Released to share these architectural efforts.</p>
+    <p class="small-print">Why Agate: LogoLabs' search for the best small-scale image architecture, as groundwork for glyph and symbol generation and vector-native fonts to go with our wordmarks. General image generation served as a benchmark-friendly proxy for those tasks (unlike, e.g., SDF generation); next it feeds SVG generation with Inkvec. Released to share these architectural efforts.</p>
     <p class="label dim">
       <a href="https://logolabs.org" target="_blank" rel="noopener">logolabs.org</a> ·
       <a href="https://huggingface.co/Logolabs" target="_blank" rel="noopener">huggingface.co/Logolabs</a> ·