# Detailmaster Eval

> Evaluates a text-to-image model's ability to faithfully render long, descriptive prompts. It probes fine-grained semantic alignment across character presence, attributes, spatial relationships, and scene composition, as well as overall aesthetic and alignment quality using preference models. Use when the user wants to benchmark on DetailMaster, or asks about evaluating this task. Reports CharacterPresence.

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

---


# detailmaster-eval

> Long-Text-to-Image Generation via Compositional Prompt Decomposition — Huang et al. (2026) (arXiv:2604.18258, 2026)

## What this evaluates

Evaluates a text-to-image model's ability to faithfully render long, descriptive prompts. It probes fine-grained semantic alignment across character presence, attributes, spatial relationships, and scene composition, as well as overall aesthetic and alignment quality using preference models.

## Datasets

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

## Metrics

- `CharacterPresence` **(primary)** — range: percent
  - Percentage of prompts where the generated image successfully contains the specified characters.
- `CharacterAttributes` — range: percent
  - Accuracy of matching character features to the prompt, computed separately for object, animal, and person categories.
- `CharacterLocation` — range: percent
  - Accuracy of correctly positioning specified characters in the generated image.
- `SceneAttributes` — range: percent
  - Accuracy of adhering to background, lighting, and style instructions in the prompt.
- `SpatialRelation` — range: percent
  - Accuracy of reflecting specified spatial relationships between characters.
- `CLIPScore` — range: other
  - Reference-free metric measuring text-image alignment using CLIP embeddings.
- `DenScore` — range: other
  - Preference score evaluating overall text-image alignment.
- `PickScore` — range: other
  - Preference score based on user preference data for text-to-image generation.
- `VQAScore` — range: other
  - Score from a multimodal LLM evaluating finer detail alignment between image and prompt.
- `HPSv3` — range: other
  - Human Preference Score v3, evaluating wide-spectrum human preference for generated images.

## Input / output format

**Input**: Long-form text prompts (paragraphs, averaging ~285 tokens) describing scenes, characters, attributes, and spatial relationships.

**Output**: A single RGB image generated from the text prompt.

## Scoring recipe

```python
def compute_accuracy(predictions, gold):
    correct = 0
    for pred_img, gold_attrs in zip(predictions, gold):
        if check_attributes_match(pred_img, gold_attrs):
            correct += 1
    return (correct / len(predictions)) * 100

def compute_preference_score(predictions, gold_prompts):
    scores = [get_model_score(pred_img, prompt) for pred_img, prompt in zip(predictions, gold_prompts)]
    return sum(scores) / len(scores)
```

## Common pitfalls

- Models trained on short prompts degrade sharply on prompts exceeding 500 tokens, so length-stratified evaluation is necessary.
- Simple sentence splitting for decomposition loses global context and yields broken scenes compared to learned compositional methods.
- Evaluations must be reported separately for different backbone architectures (e.g., SD-1.5 vs. Qwen-Image) as performance varies significantly.

## Evidence (verbatim from paper)

> Table 1 summarizes the benchmark evaluations of DetailMaster, where we examine PRISM against specialized Long-Text-to-Image generation methods and SOTA baselines. PRISM-SD1.5 outperforms other methods by 2.33% on CharacterPresence and 1.53% on CharacterLocation, demonstrating the PRISM’s efficiency in processing descriptive paragraphs.

## Citation

```bibtex
@misc{huang2026prism,
  title={Long-Text-to-Image Generation via Compositional Prompt Decomposition},
  author={Huang et al. (2026)},
  year={2026},
  note={arXiv:2604.18258}
}
```

- arXiv: 2604.18258