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lee101/zimage-latent-teleport-cache-adapter
zimage-latent-teleport-cache-adapter is a machine learning model from lee101. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Experimental 7,312-parameter residual head for cache-guided Z-Image Turbo latent teleportation. It starts from the fitted scalar local-plus-cache gate and learns a spatial correction from:
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From the Hugging Face model README
Experimental 7,312-parameter residual head for cache-guided Z-Image Turbo latent teleportation. It starts from the fitted scalar local-plus-cache gate and learns a spatial correction from:
The output projection is zero-initialized, so an untrained head exactly matches the scalar cache gate.
Implementation, training scripts, and the paper are in lee101/cutedsl.
The checkpoint was trained on 160 trajectories and evaluated on a disjoint 40-trajectory fold across valid scheduler cells and horizons 1, 2, 4, and 8.
| Method | relL2 |
|---|---|
| Calibrated local momentum | 0.2320 |
| Scalar pruned-cache gate | 0.2250 |
| Learned cache adapter | 0.2225 |
That is a 4.09% reduction versus local momentum and 1.11% beyond the scalar cache gate.
For six held-out step-6 to step-14 endpoint decodes:
| Method | PSNR | SSIM |
|---|---|---|
| Local momentum | 21.48 dB | 0.7731 |
| Scalar pruned-cache gate | 21.84 dB | 0.7881 |
| Learned cache adapter | 21.92 dB | 0.7877 |

The 7,312-parameter head takes 3.28 ms per 64x64 latent on the recorded one-thread resident CPU benchmark. Training took 132.4 seconds on CPU. An RTX 5090 run was attempted, but the safety gate deferred it because production workloads left less than 2.5 GiB allocatable VRAM; the checkpoint therefore does not claim a GPU training time.
cache_adapter.safetensors: adapter parameters.config.json: architecture and retrieval protocol.schedule_coefficients.json: momentum and scalar-gate coefficients by step
and horizon.metrics.json: training history, horizon breakdown, timing, and decoded-image
summary.This is an adapter for the CuteDSL latent-teleportation implementation, not a standalone image model. It is fitted specifically to Z-Image Turbo at 512px with the recorded 16-step schedule. A new model, solver, resolution, or schedule requires new trajectories and refitting. The decoded comparison is an offline endpoint forecast; a fully branched sampler benchmark remains separate.