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
- Image generation speed: 2026 A100 Stable Diffusion XL Batch 16 beats 32 September 6, 2026
- 2026 FID vs PickScore: CLIP Triages SDXL, Inception Can't September 4, 2026
- FID Cannot Score One SDXL Image: COCO 30K A100 Verdict September 3, 2026
- Batch Image Inference Audit: Intercepting Silent Failures September 1, 2026
- Scheduled Batching Cuts Diffusion Inference Cost by 38% August 31, 2026
- Probate Lead Staleness: The 45-Day Clock and Fresh vs. Aged August 29, 2026
- Short-Term Rental Profit in 2026: The 50% Fixed-Cost Ratio Test August 27, 2026
- August 26, 2026