Logan Hughes
AI Virtual Staging at colossis.io
Logan Hughes is a PhD candidate in Computer Vision at Stanford University, where his research focuses on generative image models and scalable visual AI pipelines. He investigates evaluation metrics for synthetic imagery and optimization of batch inference workflows for production systems. Deep experience. Intellectual curiosity.
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Recent articles by Logan Hughes
- FID vs KID for Text-to-Image: Sample Floors and Reporting Rules October 16, 2026
- Set Linux to Performance Mode: 64-Image Batch Under 100 ms, Not Powersave October 13, 2026
- AI image costs for blogs: 2026 inference price per asset vs batch latency October 10, 2026
- Virtual staging guide: Sold Atlanta open‑concept home in 7 days — Ticket or Verify October 7, 2026
- How to Choose a GPU Image-Inference Batch Without Assuming 30% Savings October 4, 2026
- Text to image quality scores 2026: Frechet Inception Distance (FID) vs CLIPScore 5K pick October 1, 2026
- Human Image Preference Test: 137,000 Choices—ImageReward Wins Pairwise Preference September 28, 2026
- Image quality test: Frechet Inception Distance (FID) vs text score, 1,000 reviews September 25, 2026