It’s easy to run the experimental Image Generation workload, but there are a few points to keep in mind:
- The workload is completely optional, and it runs separately from WebXPRT 5’s main tests. Your main WebXPRT 5 scores and testing experience will not change.
- It won’t run on everything. WebXPRT’s main tests run on just about anything with a browser. Because the Image Generation workload is a cutting-edge, resource-intensive, experimental performance test, it won’t cover that same broad range of devices. It will best suit late-model, high-performance laptops and desktops. We recommend testing on systems with at least 32GB RAM and a minimum of 8 CPU cores.
- It will require a large one-time model download. The first time you run the Image Generation workload on a specific system, WebXPRT will download the Z-Image-Turbo model from the BenchmarkXPRT Hugging Face repository. The total size of the model download is just under 6.5 GB, so depending on your internet speed, the download can take 5 to 20 minutes.
- Expect longer runtimes than a typical WebXPRT 5 test. After the model download, each run may take about five minutes.
When you’re ready to run the Image Generation workload, follow these steps:
- You don’t want the device to shut down during the tests, so go to Settings and set the sleep time to at least half an hour. This should cover the time it takes to download the model and run the tests.
- Close all your other apps and windows. Other activity on your device might lower your score.
- Go to WebXPRT.com and click "Run WebXPRT 5." Click “Continue” in the popup box.
- Click the “Experimental AI workloads” button below WebXPRT 5’s normal “Start test” button.
- On the Experimental AI workloads page, make sure that the checkbox for the Image Generation workload is checked, and click “Start test.”
WebXPRT will then initiate the model download. Once the download is complete, the workload will automatically produce four prompt-based images: an initial warm-up image and three successive scored images.
If the Image Generation workload successfully completes, it will report a single score, measured in images per minute. Due to the nature of GenAI model technology, the workload unavoidably generates slightly different image outputs each time, so no two runs look exactly alike. That said, the outputs are stable enough to produce consistent scores with low variance.