We give a principled derivation of the Soft-Min-SNR weight of Crowson et al. (2024). The spread divergence of Zhang et al. (2018) convolves both compared distributions with a Gaussian kernel before taking the Kullback-Leibler (KL) divergence; applied to the per-sample local matched-Gaussian surrogate at each timestep, it yields the closed-form weight w(t,lambda) = sigma^2 / (sigma^2 + lambda). Three consequences follow. First, for variance-preserving schedules, w(t,lambda) equals a constant multiple of Soft-Min-SNR with gamma' = (1+lambda)/lambda, deriving a validated heuristic rather than introducing a new weight. Second, the same weight matches Min-SNR-gamma at leading order under gamma approximately 1/lambda, giving a cross-walk between the soft and hard reweighting families. Third, a local-geometry analysis scales an SGD-difficulty proxy by w^3 at high-SNR timesteps. Complementary to the objective-level account of Kingma & Gao (2023), who unified monotonic-in-log-SNR weightings as ELBOs of noise-augmented data, ours smooths both compared distributions rather than only the data side. Empirically, the matching rule holds on CIFAR-10 (linear and cosine) and CelebA-64 (cosine), with trajectory-wide confirmation on the cross-dataset cut: |Ours - Min-SNR| averages 0.45 FID across seven intermediate checkpoints on the seed-42 CelebA-64 trajectory, roughly 3x tighter than either reweighter's gap to DDPM. The local-geometry prediction is partially borne out: Ours converges about 21% earlier than DDPM at mid-training FID thresholds on CIFAR-10's linear schedule, where high-SNR damping headroom is largest, but this iteration-efficiency advantage does not transfer to cosine or CelebA-64, where all three methods reach similar final FIDs. Overall: final-FID parity with dataset-dependent iteration efficiency, plus a principled matching rule across the Min-SNR family.

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