Results

All six clips: 768x1344, 124 frames, 50 steps, seed 1101, same prompt, one RTX 5090. Runs 1–5 are NVFP4 W4A4 through the Nunchaku runtime with the smooth_factor interleave fix.

Offline, per modality — the measurement that decides it

Relative L2 of the quantized layer output against bf16, median over 312 layers, 4096 sampled rows per modality per layer, rank-32 branch included, same NVFP4 fake-quantizer the calibration used.

modality	lambda=1	lambda=all	lambda=video	lambda=text
video	0.0980	0.0976	0.0937	0.0981
text	0.0891	0.0871	0.1140	0.0871
audio	0.0875	0.0857	0.0975	0.0874

vs no smoothing: lambda=all −0.4 % / −2.2 % / −2.1 %; lambda=video −4.3 % / +27.9 % / +11.5 %; lambda=text +0.1 % / −2.2 % / −0.2 %.

lambda=all is the only candidate that is not negative-sum. This is 312 layers x 3 modalities of independent measurements and it is the basis for the recommendation.

End to end, one prompt

run	mean RGB	per-frame lo–hi	span	std	Δmean	PSNR vs BF16	corr vs BF16
0 BF16	36.27	27.78–45.96	18.18	43.90	—	—	—
1 plain W4A4	28.42	27.40–29.11	1.71	41.60	−7.85	15.02	0.438
2 lambda=1 +LoRA	30.43	25.50–33.72	8.21	42.76	−5.84	15.12	0.436
3 lambda=all	36.67	31.64–43.28	11.64	46.14	+0.40	14.84	0.449
4 lambda=video	24.59	21.62–26.69	5.07	27.07	−11.67	14.50	0.139
5 lambda=text	24.58	21.73–26.99	5.26	29.73	−11.69	14.56	0.196

Read this table as a coarse split, not a ranking. All five quantized runs produce coherent video — none is degraded or noisy; see contact_sheet.png. What differs is which sample the run lands in. Runs 1, 2 and 3 stay in BF16's bedroom framing (corr 0.44–0.45); runs 4 and 5 both go to a different floor-level scene (corr 0.14–0.20, and corr 0.645 to each other — they are closer to one another than either is to BF16).

Two limits worth stating rather than papering over:

n = 1 prompt. Which basin a run falls into is close to a coin flip at this sample size. The table cannot separate run 1 from run 3, and it should not be read as doing so.
lambda=text diverges end to end while being benign offline. The offline table says it is harmless; this clip says it moved. One clip is not enough to call that a real effect.

What the end-to-end runs do establish: run 3 is the only one whose mean RGB, per-frame spread and std all land on BF16's, and the two runs that diverged are not the two that the calibration's own error column ranked best. That is consistent with the offline per-modality table and inconsistent with the proportional one.

The trap this repeats

The calibration's own error column — the one in svdquant_calib_768p*.pt — ranks the runs:

	lambda=1	lambda=1 +LoRA	lambda=all	lambda=video	lambda=text
median `err_smooth_lowrank`	0.1019	0.0951	0.0942	0.0903	0.0953

lambda=video wins it. That column is measured on rows drawn uniformly from the packed sequence, and video is 98.6 % of them, so it is a video error — it cannot see the 27.9 % it charged text. Picking a calibration by that number alone selects the configuration this study rejects.

Files

0_bf16.mp4 … 5_lambda_text.mp4, grid.mp4 (all six on one timeline, captioned), contact_sheet.png (5 frames each), README.md (what each run is and why).

Scripts, on the H100 side: calib_stats_modal.py, calib_sample_modal.py, score_modal_lambda.py, score_modal_report.py, diag_lambda_crossmodal.py, lambda_exps_report.py, mk_lambda_grid.py.