README.md

---
license: cc-by-4.0
pretty_name: HareSkip Calibration Dataset
size_categories:
- 1K<n<10K
tags:
- image
- diffusion
- diffusion-transformer
- inference-acceleration
- step-skipping
- benchmark
- anime
---

# HareSkip Calibration Dataset

## 1. Summary

This dataset contains all images and measurements generated during a July 2026 recalibration experiment for **HareSkip**, a step-skipping inference-acceleration extension for DiT-based image diffusion, implemented on top of [Forge neo](https://github.com/Rootport-AI/forge-neo-Anima-HareSkip) (repository: `Rootport-AI/forge-neo-Anima-HareSkip`).

The experiment measured how much visual damage results from skipping specific denoising steps, across single-step sweeps, multi-step interaction patterns, and out-of-sample validation patterns representative of HareSkip's actual runtime behavior. The full write-up, methodology, and analysis are in the experiment report:

[`docs/recalibration-2026-07/REPORT.md`](https://github.com/Rootport-AI/forge-neo-Anima-HareSkip/blob/main/docs/recalibration-2026-07/REPORT.md) in the `forge-neo-Anima-HareSkip` repository.

This dataset consists of:

- **3,186 PNG images** (1536×1536), covering reference (no-skip) images and skip-pattern candidates across 45 conditions (3 samplers × 5 prompts × 3 seeds), plus a step-count anchor set and a Shift-transfer spot check.
- **Measurements**: 6 Judge comparison runs (LPIPS-VGG/AlexNet, SSIM, PSNR, etc.) as CSV, plus a frozen pre-registered prediction table.
- **Patterns**: the skip-pattern text files used to drive generation, including the frozen out-of-sample holdout patterns.
- **Provenance**: a fixed-commit statement, frozen-file hashes, and a full SHA-256 manifest.

The purpose of publishing this dataset is **reproducibility**: every image is traceable to an exact commit, an exact skip pattern, and an exact set of generation parameters, so that the report's findings can be independently re-derived or re-analyzed.

## 2. Dataset structure

```
images/
measurements/
patterns/
provenance/
```

### `images/` (3,186 PNG files, 1536×1536)

| Directory | Images | Content |
| --- | ---: | --- |
| `Reference-images02/` | 45 | No-skip reference images, one per condition (3 samplers × 5 prompts × 3 seeds), regenerated on the fixed commit |
| `Calibration-phase1-02/` | 1,305 | Stage 1: single-step skip sweep (steps 2–30, 29 positions × 45 conditions) |
| `Calibration-stage2/` | 1,260 | Stage 2: interaction patterns (adjacent pairs, distance scans, cross-band pairs, chains, triples — 28 patterns × 45 conditions) |
| `Stage3-Calibration/` | 450 | Stage 3: out-of-sample validation against HareSkip's actual runtime patterns (10 patterns × 45 conditions) |
| `StepAnchor-images/` | 90 | ±1-step anchor: no extension, total step count varied to 29/31 only (45 conditions × 2) |
| `ShiftSpotcheck-Reference/` | 6 | Reference images for the Shift-transfer spot check (Shift=1) |
| `ShiftSpotcheck-Calibration/` | 30 | Shift=1 candidates at 5 characteristic curve points × 6 conditions |

Step 1 is excluded throughout: Manual Skip mode rejects step 1 (its residual is not yet held, so it is physically unskippable).

### `measurements/`

| Path | Content |
| --- | --- |
| `judge-results/<run>/merged-results.csv` + `run-config.json` (6 runs) | Nz DoppelPix Judge comparison output: LPIPS-VGG (primary metric), LPIPS-AlexNet, SSIM (win 7 / win 11), PSNR, per candidate-vs-reference image pair |
| `predictions/stage3-predictions.csv` | Frozen pre-registered prediction table for the 450 Stage-3 out-of-sample points (predicted before generation; SHA-256 below) |
| `predictions/fit_params.json` | Frozen saturation-link fit parameters used to produce the predictions |

The 6 included runs are: `Calibration-phase1-02_20260718-150151`, `Calibration-phase1-02-remainder_20260719-160107`, `StepAnchor_20260727-075911`, `Calibration-stage2_20260727-081812`, `ShiftSpotcheck_20260729-084448`, `Stage3_20260729-085016`. All completed with zero metrics errors.

An earlier measurement run (`Calibration-phase1_20260716-234016`) and its associated images/analysis are **excluded** from this dataset: they were generated before a Forge-neo version confound was discovered (see §4) and would mislead reproducibility checks if published alongside the corrected data.

### `patterns/`

Skip-pattern definitions used to drive Manual Skip generation, plus the prompt set:

| File | Content |
| --- | --- |
| `layer1-single-skip-sweep-30steps.txt` | Stage 1 patterns (29 lines) |
| `stage2-interaction-patterns.txt` | Stage 2 patterns (28 lines) |
| `stage3-holdout-patterns.txt` | Stage 3 out-of-sample holdout patterns (10 lines, frozen; SHA-256 below) |
| `stage3-holdout-patterns-REPORT.md` | Explanation of how the Stage 3 patterns were derived and verified |
| `stage3-holdout-simulate.py` | Offline simulator used to enumerate the Stage 3 patterns from HareSkip's probability model |
| `prompt.txt` | The 5 prompts used across all conditions |

### `provenance/`

| File | Content |
| --- | --- |
| `PROVENANCE.md` | Fixed-commit statement, frozen-file hashes, excluded-data rationale, included-run list |
| `manifest.sha256` | SHA-256 checksums for every file in the dataset (`sha256sum`-compatible format), 3,206 entries |

## 3. Self-describing images

Every PNG carries a `tEXt` chunk keyed `parameters`, in Forge's standard infotext format, embedding the full generation condition — prompt, negative prompt, seed, sampler, schedule type, CFG scale, **Shift**, model and hash, HareSkip mode, ResRefine formula, and the **realized** skipped-step set (`Manual skipped_steps`). Example (from `images/Calibration-phase1-02/ERSDE-Beta_Prompt001-schoolgirl-5193/00000-3000995193.png`):

```
masterpiece, best quality, score_7, safe, a girl with demon horns is sitting indian style on ground, holding a cat. ...
Negative prompt: worst quality, low quality, score_1, score_2, score_3, artist name
Steps: 30, Sampler: ER SDE, Schedule type: Beta, CFG scale: 4.0, Shift: 3.0, Seed: 3000995193, Size: 1536x1536, Model: anima_baseV10, Model hash: bd43b7cffe, Module 1: qwen_image_vae, Module 2: qwen_3_06b_base, RNG: CPU, HareSkip enabled: True, HareSkip mode: Manual Skip, ResRefine formula: Reuse (residual only), Beta schedule alpha: 0.6, Beta schedule beta: 0.6, Manual skipped_steps: 2, Version: neo-2.27
```

`Manual skipped_steps` is written by the patcher from the steps it actually skipped during generation, not from the requested input — it is a realized value, not a nominal one. This makes every image self-describing on its own: the generation condition can be recovered from the file alone, with no dependency on directory naming or an external index. Dataset-wide acceptance testing cross-checked this realized value against file name and pattern canon for all 3,186 images.

## 4. Reproducibility & integrity

All 3,186 images were generated on a single, fixed Forge neo commit: **`b61642140acb7c2f1c65c5d0f2ab961b7366c02e`** (reported in PNG metadata as `Version: neo-2.27`). This fix was adopted after an earlier measurement round was found to be confounded by an undocumented Forge-neo version drift between reference and candidate images (see the source report, §2.2, for the full incident writeup); that earlier round is excluded from this dataset (§2 above).

### Integrity verification

Every file in the dataset is listed in `provenance/manifest.sha256`. To verify the full download:

```bash
sha256sum -c provenance/manifest.sha256
```

### Frozen hashes and pre-registration

Stage 3 (out-of-sample validation) followed a pre-registration discipline: predictions were frozen *before* the corresponding images were generated or measured.

| File | SHA-256 (first/last 8 hex) |
| --- | --- |
| `patterns/stage3-holdout-patterns.txt` | `659A7445...7CD3` |
| `measurements/predictions/stage3-predictions.csv` | `F8495DD1...FA131D` |

(Full 64-character hashes are recorded in `provenance/manifest.sha256`.) The Stage 3 holdout patterns were derived by offline simulation of HareSkip's probability model (`sigmoid_band_v0.1`) and independently verified against the extension's own code before generation. The prediction table's parameters were fit only on Stage 2 data; re-fitting on Stage 3 data was explicitly disallowed. This lets a third party check that the predictions in this dataset were not adjusted after seeing the outcomes they are being judged against.

## 5. Model and licensing

The images in this dataset were generated with **Anima (Anima-Base v1.0)** by CircleStone Labs & Comfy Org (https://huggingface.co/circlestone-labs/Anima), a derivative of NVIDIA Cosmos-Predict2-2B-Text2Image ("Built on NVIDIA Cosmos"). The exact model file used has SHA-256 `bd43b7cffe1ed1153d9c41e7beb2f18cb1273eafbaa3af3edd6a173dc90a006e`, which matches the `Model hash: bd43b7cffe` recorded in every image's infotext (verified against the dataset).

**License note:** The Anima model weights themselves are distributed under the CircleStone Labs Non-Commercial License v1.2 (non-commercial use only for the weights). However, that license's §2.e addresses Outputs (generated images) explicitly:

> "We claim no ownership rights in and to the Outputs. ... You may use Outputs for any purpose (including for commercial purposes)."

Accordingly, **this dataset — the collection of Outputs plus the original measurements produced by this experiment — is released under CC-BY 4.0.** This license covers the images and measurement data in this repository. It does not cover the Anima model itself; anyone wishing to use the Anima model weights to generate new images must comply with CircleStone Labs' own license terms.

## 6. What this data shows

- Damage from skipping a step is concentrated in the early denoising steps (Spearman ρ = −0.784 between step position and damage), with the worst position typically at skip 2–4 rather than the very first skippable step.
- A simple predictive formula — the sum of single-step damages passed through a two-parameter saturation link — passed pre-registered out-of-sample validation on 450 held-out points (Spearman ρ = 0.930), with no need for a streak (consecutive-skip) penalty term.
- Predicted damage carries a consistent safe-side bias (actual damage tends to be ≤ predicted), which is favorable for quality-guarantee use but means absolute-value predictions should not be taken as tight bounds.
- The hypothesis that damage is governed purely by trajectory coordinate z (independent of the Shift parameter) did not hold cleanly: changing Shift from 3 to 1 changes the shape (steepness) of the damage curve, not just its position.

See [`docs/recalibration-2026-07/REPORT.md`](https://github.com/Rootport-AI/forge-neo-Anima-HareSkip/blob/main/docs/recalibration-2026-07/REPORT.md) for full methodology, statistics, and discussion.

## 7. Citation

```bibtex
@misc{rootport2026hareskipcalibration,
  author       = {Rootport},
  title        = {HareSkip Calibration Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Rootport/HareSkip-calibration}},
}
```

---

## 日本語要約

本データセットは、DiTベース画像生成の高速化拡張「HareSkip」（Forge neo拡張、リポジトリ: `Rootport-AI/forge-neo-Anima-HareSkip`）の較正実験で生成した全画像・測定値です。固定コミット（`b6164214`、neo-2.27）で生成した3,186枚のPNG（1536×1536）と、LPIPS/SSIM等の測定CSV、パターン正典、出自記録一式を収録しています。各PNGのinfotextに実現スキップ位置を含む生成条件が完全に埋め込まれており、画像単体で再現性を検証できます。生成モデルはAnima（CircleStone Labs & Comfy Org、NVIDIA Cosmos派生）で、モデル本体は非商用ライセンスですが、同ライセンス§2.eにより生成画像（Outputs）の商用利用を含む自由な利用が明記されているため、本データセット（Outputs＋独自測定値）はCC-BY 4.0で公開します。詳細な分析結果（損傷の序盤集中、予測式のout-of-sample検証、Shift依存性など）は元リポジトリの実験レポートを参照してください。