# HoLo-FuSe — class-conditional diffusion on the 0-parameter HSL byte substrate (minimal-scale baseline, honest results) > Source: > Published: 2026-07-12 14:51:29+00:00 Following up on the HoLo line (byte-LM, speech): **HoLo-FuSe** tests whether the same frozen HSL substrate can serve as the **conditioning door of an image-generation carrier**. Same method as the other rooms: take a verified baseline, swap exactly one door for HSL, measure against controls. **Honest framing first.** This was trained on a single free Colab T4 (~35M U-Net, 16k steps/arm, 128px). It is a **minimum-scale baseline run** — the point is *proof of operation*, not visual quality. Please read the samples with that in mind; compute, not the method, is the main quality ceiling here. HSL is a frozen, deterministic 27-D feature frame over bytes (value geometry + cross-byte flow + boundary + Fourier + phase; a 4.6 KB LUT, **0 learned parameters**). Per the family rule — *fixed substrate where possible, explicit lens where necessary* — the only lens this room needs is a **label→condition readout**: `label bytes -> frozen 27-D HSL frame (0 params) -> small learned readout (2-layer MLP) -> condition embedding -> added to the DDPM timestep embedding` No spatial lens: the conv U-Net carrier already owns spatial structure. The substrate stays frozen; the readout is the only trained conditioning component, and it is budget-matched to the control. Class-conditional DDPM (cosine schedule T=250, multi-level U-Net + self-attention, EMA, classifier-free guidance with cond-drop 0.15). Data: AFHQ animal faces at 128px, Cat 5153 / Dog 4739 (CC BY-NC 4.0 — so weights and samples are **non-commercial**). Three arms, **same seed, same data, same architecture surface, same budget**, compared at step 14000: | arm | conditioning | result (qualitative, seed-matched) | |---|---|---| `none` | unconditional baseline | readable cat+dog faces, classes mixed | `learned` | same-budget `nn.Embedding` control | “Cat”→cats, “Dog”→dogs | `hsl` | frozen HSL 27-D (0 learned params) + readout | “Cat”→cats, “Dog”→dogs | Two observations from the seed-matched bench (image attached; positions share the same initial noise): The plan is to focus on **quality of this one model** (longer training, color balance, multi-seed evaluation) rather than adding more models. That said, the compute budget is what it is — a free T4 and a 4 GB laptop GPU — so please keep expectations modest. If the substrate claim survives better-trained carriers, that is the result we are after; prettier cats are a bonus. Code, training harness, bench scripts, and the POC record: [https://github.com/Woojiggun/HoLo-FuSe](https://github.com/Woojiggun/HoLo-FuSe) Live demo (ZeroGPU, generates in seconds; checkpoints are linked from the repo):