HoLo-FuSe — class-conditional diffusion on the 0-parameter HSL byte substrate (minimal-scale baseline, honest results)

HoLo-FuSe, a class-conditional diffusion model using a frozen 27-D HSL byte substrate with zero learned parameters, successfully generated cat and dog faces from AFHQ data at 128px resolution on a single Colab T4. The minimal-scale baseline run demonstrated proof of operation, matching the performance of a same-budget learned embedding control while using no learned conditioning parameters.

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 :