Image diffusion models draw decent stick figures, yet speech-bubble text still melts into unreadable soup. Over the weekend I trained and quantized xkcd-artist to separate neural stage planning from vector ink lettering. What it does: - 1.72 MB Int8 ONNX kinematic planner running in-browser via WASM SIMD in 0.55ms P50 (trained on NVIDIA L4 across 226 speech-masked panels + 891 ExplainXKCD dialogues) - Predicts panel count, stage props, 12-DOF stick-figure poses, and pen wobble; procedural SVG compositor renders monochrome wobbled strokes, curved speaker leader lines, crisp xkcd-script dialogue, and hover title text - Clause-aware deadpan generator covering 138+ unique comic strips, data charts, and flowcharts (98.33% skeleton uniqueness across 120 prompts, B = 1,000 bootstrap eval) Live studio (zero cloud calls): https://h3manth.com/fun/xkcd-artist/ Hugging Face weights (1.72 MB Int8 ONNX): https://huggingface.co/gnumanth/xkcd-artist GitHub (MIT, npm i xkcd-artist / pip install xkcd-artist): https://github.com/hemanth/xkcd-artist Would love feedback!   submitted by   /u/init0 [link]   [comments]