It is aimed at the annoying middle ground: sprites, emblems, and logos that look locally pixelated but fall apart the second you ask them to commit to one lattice like grown-ups.
Instead of pretending this is a resize problem, Repixelizer treats it as lattice inference plus local structure preservation: infer the implied grid, choose real output cells, and snap them back onto a coherent pixel lattice while preserving the source's local adjacency patterns.
Repixelizer was built to rescue fake pixel art, but it can also be used to generate pixel art directly from non-pixel source art when the shapes are clean and the local structure is doing something useful.
Rows show the source art, a plain Lanczos downscale to the same target size, and the repixelized result. The bottom row also carries a sword-guard picture-in-picture, because that tiny region is one of the more honest tests of whether the tool is preserving local cell adjacency instead of just getting the vibes approximately correct.
That is the two-headed pitch in one image: force non-pixel art onto a coherent grid, or take AI pixel art that only respects the grid locally and make it commit.
This repo is past the "pile of hopeful heuristics" stage and into "real machine, still experimental."
What exists now:
- lattice inference with CUDA support
- a lean displacement-field optimizer in `src/repixelizer/phase_field.py`
- a source-owned alternate solver in `--reconstruction-mode tile-graph`
- automatic diagnostics, comparisons, and benchmark runs
- a tuning harness for offline parameter sweeps
- metrics that finally care about visible structure instead of only pleasing the lattice accountant
What changed recently:
- the old tray-based optimizer is gone
-`phase-field` is now the default optimizer path
-`source_structure` is reported alongside `source_fidelity`, because the old metric was happily calling better-looking images worse
- the tracked sword-tip blemish on the AI badge has its own focused fixture in `tests/fixtures/real/ai-badge-tip-focus.json`
Current read on the engines:
-`phase-field` is the main line now. It is the best-looking badge result the repo has produced so far, especially on internal structure, even though it still widens the sword-tip stroke a bit too much in the tracked focus crop.
-`tile-graph` is still useful and still very real, but it remains slower and more temperamental on messy badge-scale inputs. Its best full-emblem probe under `artifacts/full-emblem-tile-graph-atomic-v3-cuda/` is still a strong result.
Current weak spots:
-`phase-field` still needs better along-stroke versus across-stroke behavior near tapered contours
- tile-graph cold-build time is still dominated by connected-component labeling / region extraction on large fixtures
- lattice selection is still low-confidence on some ugly generated inputs, so pinned-size iteration remains an important workflow
This is intentionally a search-based tuner, not gradient descent. The objective depends on discrete argmins, thresholding, and benchmark comparisons, so it is better treated as a repeatable black-box optimization problem.
That keeps benchmark assets, attribution exports, and tuning runs from polluting upstream history while still keeping the code, docs, and corpus layout tracked.
That regenerates the README sheet, the standalone sword-guard closeup strip, and scratch outputs under `artifacts/readme-build/` so you can inspect the actual low-res results used to build the docs sample.
