aszc-dev-ComfyUI-CoreMLSuite/docs/spikes

Phase 7 — Strategic spikes (research only)

Research-only investigations per MODERNIZATION_SPEC.md Phase 7. None of these are merged behavior changes. Each spike delivers a written recommendation with effort/payoff so the maintainer can choose which (if any) become real phases.

#	Spike	Verdict	Effort	Payoff
1	MultiFunction models	Prototype for controlnet-on/off only; use enumerated shapes for batch	Medium	Medium (gated by macOS 15)
2	Flexible / enumerated shapes	EnumeratedShapes for spatial size; keep chunk_batch for batch	Medium	Medium–High
3	MLX interop decision	Stay pure Core ML/ANE. Document an off-ramp; do not absorb MLX	Trivial (docs)	High (positioning)

Cross-cutting finding

Spikes 1 and 2 both run into the same wall: the high-value packaging features (MultiFunctionDescriptor, materialize_dynamic_shape_mlmodel, multi-input EnumeratedShapes) require macOS 15 / iOS 18 at runtime, while the suite today targets macOS 13 (coreml_suite/converter.py:110). Adopting either means either raising the floor (drops older-OS users — a real cost for a community ComfyUI pack) or keeping the current per-config path as a fallback (erodes the "one artifact" simplicity win). This OS gate, not the API, is the dominant decision factor.

Gate 7 report

## Gate 7 report
- Spike 1 (MultiFunction): recommendation written — prototype controlnet-on/off
  as 2-function package; NOT for batch size. Gated macOS 15. No code merged.
- Spike 2 (flexible shapes): recommendation written — EnumeratedShapes for
  spatial size, retains ANE per coremltools maintainer confirmation; keep
  chunk_batch for batch. No code merged.
- Spike 3 (MLX interop): recommendation written — stay pure Core ML/ANE,
  document off-ramp to mflux/Mflux-ComfyUI for Flux. Reject in-suite interop.
  No code merged.
- Open verification gaps requiring on-hardware [M2-ANE] measurement are listed
  per spike under "Unverified".

Verification debt (must measure on [M2-ANE] before any promotion)

• EnumeratedShapes ANE residency for a real SD UNet (one unreproduced coremltools regression report exists — spike 2).
• Multifunction SD UNet ANE behavior + first-load compile time (no public report either way — spike 1).
• Full-pipeline diffusion power draw ANE vs MLX-GPU (only GEMM-level figures exist — spike 3). bench/run.py already measures CPU_AND_NE vs CPU_AND_GPU, so this is the highest-value artifact to make the moat quantitative.