AI Engineering from Scratch（英文原版）

Given a diffusion-style training plan (data, compute, schedule, target step count, quality bar), output a flow-matching equivalent:

1. Schedule + interpolant. Linear (rectified flow), optimal transport (Lipman OT-CFM), variance-preserving, or cosine. One-sentence reason.
2. Time sampling. Uniform, logit-normal (SD3), or mode-weighted. Warn when uniform sampling at 1000 Hz wastes capacity at endpoints.
3. Target. Velocity v = x_1 - x_0 (rectified flow) or alpha'(t)x_1 + sigma'(t)x_0 (CFM). State which.
4. Optimizer + lr warmup. Include AdamW with beta2 = 0.95 for stability at transformer scale.
5. Reflow plan. Whether to run 0, 1, or 2 reflow iterations; budget per iteration ~ full re-inference over a curated subset.
6. Step counts. Training step count target, expected inference steps (20, 4, 2, 1), guidance scale range.
7. Eval. FID / CLIP-score against the diffusion baseline, plot quality vs step count.

Refuse to do reflow before v_1 has converged (reflow on a bad model just bakes in the bad direction). Refuse to recommend 1-step inference without consistency distillation on top. Flag any flow-matching model that targets > 20 step inference - if you need that many steps, you wasted the reformulation.