# Stablei2i Eval

> Evaluates multimodal models' ability to detect unintended content, structural, and low-level appearance changes in image-to-image transitions. It probes fine-grained visual reasoning and pixel-level alignment capabilities by asking models to assess fidelity across three distinct dimensions. Use when the user wants to benchmark on StableI2I-Bench, or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/stablei2i-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/stablei2i-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/stablei2i-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/stablei2i-eval

---


# stablei2i-eval

> StableI2I: Spotting Unintended Changes in Image-to-Image Transition — Li et al. (2026) (arXiv:2605.04453, 2026)

## What this evaluates

Evaluates multimodal models' ability to detect unintended content, structural, and low-level appearance changes in image-to-image transitions. It probes fine-grained visual reasoning and pixel-level alignment capabilities by asking models to assess fidelity across three distinct dimensions.

## Datasets

- **StableI2I-Bench** — total ?; splits: test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of correctly predicted answers (and problem types for Binary & Type QA) out of the total number of samples. Reported per dimension (Structure, Semantic, Low-level) and as an average.

## Input / output format

**Input**: Image pair $(I_{in}, I_{out})$ and an I2I control instruction $x$, formatted within a fixed task template/prompt.

**Output**: QA response indicating fidelity status (e.g., 'Yes'/'No' for error presence) and a problem type description.

## Scoring recipe

```python
def compute_accuracy(predictions, gold):
    correct = 0
    for pred, g_ans, g_type in zip(predictions, gold['answer'], gold['type']):
        if pred['answer'] == g_ans and pred['type'] == g_type:
            correct += 1
    return correct / len(predictions)
```

## Common pitfalls

- Model performance is highly sensitive to the prompt template; removing structured priors causes significant drops in accuracy for general-purpose MLLMs.
- Structure-level evaluation requires pixel-level alignment, which is inherently difficult and often leads to low scores even when semantic content is preserved.

## Evidence (verbatim from paper)

> The reported values are the accuracy of samples where both the answer and the problem type in Binary & Type QA are predicted correctly.

## Citation

```bibtex
@misc{li2026stablei2i,
  title={StableI2I: Spotting Unintended Changes in Image-to-Image Transition},
  author={Li et al. (2026)},
  year={2026},
  note={arXiv:2605.04453}
}
```

- arXiv: 2605.04453