AI Photos

We'd reach for this when a local rig already exists and the symptom is fuzzy: renders feel sluggish, batches die with out-of-memory, or a workflow that ran fine last week now crawls. The value is the measurement discipline. Timing only a warm generation, keeping model, sampler, step count, resolution, batch and precision fixed, then changing one factor is the part most people skip, and it is the part that actually isolates the bottleneck. When to pick it: you run ComfyUI or a similar local stack and want a repeatable test rather than vibes. The scenario matrix is the sleeper feature, because inference, big-batch/high-resolution passes and LoRA training stress memory very differently, and each has its own metric worth recording. In practice that stops you from tuning precision to fix what is really a paging problem during model switches. When to pass: if you haven't built the machine yet and want model recommendations with VRAM figures, this page sends you elsewhere on the same site. Complete beginners who need install walkthroughs will find the diagnostic framing premature. And if you're comparing cloud AI photo services on price or features, this is the wrong page entirely. Where it bites: the guide deliberately avoids naming GPUs and won't hand you gigabytes-per-model tables, so you have to do the recording yourself. That is a real ask, and the payoff only shows up if you actually run the controlled sequence instead of reading it and nodding. Closest alternative for most people is a benchmark spreadsheet or a community thread on out-of-memory fixes. Those give you someone else's configuration; this gives you a method for yours. We'd keep it bookmarked next to the site's CPU-GPU calculator and the VRAM and bandwidth guide rather than in place of them.