repixelizer/docs/implementation-plan.md

Repixelizer Implementation Plan

What this file is

This file is the forward plan and active hypothesis ledger for Repixelizer.

It is not the authoritative stage-by-stage control-flow map. That lives in docs/lean-optimizer-algorithm-map.md.

If these two notes disagree about what the code does right now, trust the algorithm map and the source, then fix this plan instead of hand-waving about intent.

Current machine

Repixelizer now has one live reconstruction engine:

• phase-field: the canonical reconstruction engine in src/repixelizer/phase_field.py

The live pipeline is:

source image -> lattice inference -> edge analysis -> phase-field reconstruction -> cleanup -> optional palette fit -> diagnostics

Inference now splits three ways:

• fixed lattice when the caller pins size and/or phase explicitly
• searched lattice for the full local workflow
• direct autocorr lattice for the hosted demo path: one consensus size, one cheap phase probe, no size search, no phase rerank

The hosted web shell now also has an auth seam:

• src/repixelizer/access.py owns the app-local access boundary and local ownership checks
• src/repixelizer/gui.py owns hosted route policy, landing-page behavior, and queue/job stamping
• self-host and local runs stay permissive by default unless GC_ACCESS_* turns the seam on

What is working

• lattice size and phase inference are still shared and still CUDA-capable
• phase-field is the only canonical reconstruction path and produces the best-looking badge result in the repo so far
• compare mode, benchmark mode, diagnostics writing, and tuning all still work after the optimizer cutover
• the repo now reports both:
  • source_fidelity: agreement with the inferred lattice portrait
  • source_structure: visible structural agreement at source size

That second metric exists because the first one was happily slandering the better-looking output.

Current evidence

Phase-field

Pinned badge case:

• fixture: tests/fixtures/real/ai-badge-cleaned.png
• lattice: 126x126
• phase: (0.0, -0.2)
• steps: 48

Useful artifacts:

• first pinned badge run: artifacts/phase-field-v1-badge-126/
• tip-focused follow-up: artifacts/phase-field-v2-badge-126/
• structure-metric sanity check: artifacts/phase-field-metric-check/

What we know:

• the output preserves the important badge structure better than the deleted optimizer did
• the tracked blemish is now narrow and specific: the sword-tip stroke widens too much in one local region
• the tracked focus fixture is tests/fixtures/real/ai-badge-tip-focus.json

Maps

The repo now keeps the living map:

• docs/lean-optimizer-algorithm-map.md

If a future pass cannot be explained cleanly against that map, it should not land.

Current priorities

1. Fix the phase-field sword-tip blemish

Goal:

• keep the current structural win
• stop widening the dark contour near the tapered sword tip

Current hypothesis:

• scalar weight nudges are not enough
• the field needs better anisotropic behavior near sharp tapered contours
• it likely needs to distinguish motion along a stroke from motion across a stroke

What we just learned:

• the later d9fa411 phase-field tuning pass was a real regression, not paranoia
• that pass strengthened the local edge penalty, added a spacing loss plus upper-spacing clamp, and made edge gating more aggressive
• on the pinned badge case, it removed internal linework and changed 2433 / 15876 output cells relative to the original good phase-field run
• reverting that tuning pass restores the original good badge result exactly; the fresh fixed run under artifacts/phase-field-regression-fix-badge-126/ is byte-identical to the original artifacts/phase-field-v1-recheck-badge-126/

Tracked fixture:

• tests/fixtures/real/ai-badge-tip-focus.json

2. Keep lattice inference honest

Current stance:

• keep the shipped searched path grounded in autocorrelation only
• let autocorr keep a small near-best lag plateau and use cross-axis consensus before collapsing to one lattice family
• the hosted demo path now skips multi-size search entirely and uses one autocorr-consensus size plus the cheap inferred phase, capped by the hosted output limit
• do not trust the old pixel-walk / change-interval spacing path; it was cut after it kept pruning the badge away from the 126 family
• do not keep the projected edge-energy spectral pass; it floor-hugged to 2px / 626-family nonsense on the dense landscape fixture and did not earn its keep
• treat phase rerank as optional scaffolding, not sacred machinery

Future options worth exploring, if autocorr stops carrying its weight:

• distance-transform / medial-radius blob sizing on softened interiors
• small scale-space blob-size detection (LoG / DoG)
• blur-aware correction of observed blob width before converting to lattice size
• tiny learned size-and-phase prior trained on synthetic fake-pixel-art fixtures

3. Keep phase-field honest

Rules:

• do not reintroduce portrait layers, candidate trays, or solver-stage religions
• if a new term lands, it must belong cleanly in the one-field objective
• if a result looks better and the metrics disagree, fix the metrics rather than worshipping them

4. Keep hosted auth surgery out of the solver

Current stance:

• Heimdall integration belongs in the hosted web layer, not in the reconstruction pipeline
• queued jobs now have local owner metadata so future auth can resolve job access from local session/account instead of provider ids
• the repo is only "auth-ready", not "auth-landed"
• GC_ACCESS_MODE=trusted-header exists as a thin local seam for future integration and route-policy tests; it is not the final Heimdall story

Guardrails

• prefer one clear hypothesis per pass
• show the output after every real reconstruction run
• keep the maps updated
• revert or delete machinery that does not visibly earn its keep

This repo already spent enough time as a Jenga tower of maybe-useful cleverness. The machine has to deserve every part.