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 Live demo (ZeroGPU, generates in seconds; checkpoints are linked from the repo):