An automated benchmarking suite for evaluating image generation models across dimensions like prompt adherence, style, and character consistency.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-model-evaluation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-model-evaluation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image Model Evaluation?
This skill provides a systematic framework for testing and comparing AI image generation models. It automates the complex process of running standardized test cases, including text-to-image (T2I) and image-to-image (I2I) workflows, while specifically focusing on high-stakes metrics like character consistency across different poses and environments.
By using Openclaw Skills, developers can quickly assess model quality through generated visual reports and detailed performance data. The skill handles the heavy lifting of API orchestration, parameter variation, and result aggregation, making it an essential tool for AI researchers and creative developers.
Image Model Evaluation Use Cases
- Benchmark new image generation models against industry standards.
- Verify character consistency for storytelling or branding workflows.
- Compare model performance across different aspect ratios and artistic styles.
- Generate professional evaluation reports for stakeholders or clients.
- Optimize prompt engineering by testing variations across multiple scenarios.
How Image Model Evaluation Works
- Parse user requirements to identify the target model and the desired testing depth, such as Quick or Full testing suites.
- Validate model compatibility and feature support using internal reference documentation.
- Generate a comprehensive test plan and cost estimation for user approval before execution.
- Execute text-to-image tests covering dimensions, artistic styles, and complex multi-subject scenes.
- Conduct character consistency benchmarks by generating a base reference and testing it against varied poses, backgrounds, and angles.
- Aggregate raw API data, including latency, cost, and image URLs for every generated asset.
- Synthesize an interactive HTML report using standardized templates for easy visual comparison and quality scoring.
Image Model Evaluation Setup
To begin using this tool within Openclaw Skills, ensure your environment is configured with the necessary model API keys.
# Ensure your references directory is populated
# models.md should contain your target model IDs
# test-cases.md defines your evaluation criteria
The skill requires an assets/report-template.html file to be present for report generation. Output will be saved to the evaluation_output/ directory by default.
Image Model Evaluation Data Schema & Taxonomy
The skill organizes its output into a structured directory named after the model being evaluated.
| File Name | Description |
|---|---|
| index.html | The primary visual report for browser-based review. |
| t2i_*.json | Raw data for text-to-image test results including prompts and parameters. |
| i2i_*.json | Raw data for image-to-image and consistency results. |
| summary.json | A high-level metadata file containing success rates, average latency, and total costs. |
Image Model Evaluation Advanced Features
- Multi-threaded execution supporting up to 4 concurrent API requests for reduced benchmarking time.
- Advanced Character Consistency Engine that tests pose, scene, and lighting variations while monitoring facial feature retention.
- Automated budget estimation and cost prompts to prevent unexpected API expenditures.
- Custom HTML reporting with visual side-by-side comparisons for Image-to-Image transformations.
- Robust error handling with configurable retry logic for API timeouts and transient failures.
SKILL.md
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