DefaultShift: Auditing Semantic Default Shift in Accelerated Text-to-Image Models

Xuanhua Yin; Chuanzhi Xu; Shunqi Mao; Wei Guo; Weidong Cai

cs.CVAug 22, 2026

DefaultShift: Auditing Semantic Default Shift in Accelerated Text-to-Image Models

Authors: Xuanhua Yin, Chuanzhi Xu, Shunqi Mao, Wei Guo, Weidong Cai

Organizations: School of Computer Science, The University of Sydney

Abstract

Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes even when individual outputs remain plausible and aligned. We call these distributions semantic defaults and their change under replacement semantic default shift. Existing quality, preference, and diversity evaluations do not test whether a replacement preserves its reference model's semantic defaults. We introduce DefaultShift, a paired audit that labels repeated samples with closed semantic vocabularies, measures probability-mass movement, and separates interpretable ranking from confirmatory cross-fit inference. Across 14 reference and replacement pairs, adjusted color discrepancies range from 0.054 to 0.303 with recipe-specific directions. A 1,000-image human audit reproduces the ordering. We further introduce DefaultShift-Select, an offline calibration method that reduces human-measured shift by 10.3 percent to 35.1 percent across Turbo, DMD2, and FLUX without material quality loss. Under balanced evaluation, selected data recover 4.3 accuracy points and 7.5 worst-group points over uncalibrated replacement data. DefaultShift makes semantic preservation under acceleration measurable and actionable.

Figures & tables

Figure 1: The same prompt can induce different semantic defaults. A reference model and its accelerated replacement can produce individually plausible outputs while following different semantic distributions. DefaultShift measures both the magnitude and direction of this change.

Figure 2: Overview of DefaultShift. The audit generates matched samples from a declared reference and accelerated replacement, maps each image to a closed semantic vocabulary, and compares their conditional distributions. Cell-level discrepancies are aggregated for recipe ranking and directional analysis. DefaultShift-Select uses reference statistics and a quality-filtered replacement pool to construct a distribution-matched offline subset.

Figure 3: Acceleration recipes differ in magnitude, attribute, and direction. a The unified

q=64

audit covers all 14 replacement pairs. Lines are 95% object-cluster intervals. b Color dominates the four-attribute profile, while viewpoint shifts are small. c Replacement-minus-reference changes in gray and warm mass reveal opposing recipe fingerprints.

Recipe	$q=32$	$q=64$	95% CI	Recipe	$q=32$	$q=64$	95% CI
NitroFusion-4	0.269	0.303	[0.252, 0.354]	TDM-SD3	0.155	0.170	[0.136, 0.205]
Turbo	0.226	0.254	[0.215, 0.293]	PixArt-LCM	0.107	0.120	[0.092, 0.148]
DMD2	0.210	0.235	[0.196, 0.273]	Flash-SDXL	0.101	0.114	[0.088, 0.141]
NitroFusion-1	0.205	0.228	[0.184, 0.273]	Hyper-SD15	0.073	0.079	[0.061, 0.098]
FLUX-schnell	0.192	0.203	[0.169, 0.239]	SD1.5-LCM	0.060	0.066	[0.050, 0.083]
SwD-FLUX	0.171	0.187	[0.150, 0.225]	Lightning	0.040	0.060	[0.044, 0.077]

Table 1: Unified color audit. The

q=32

screen and

q=64

endpoint use identical cells. Brackets give 95% object-cluster intervals for the

q=64

endpoint.

Figure 4: Reliability and specificity. a Query-budget convergence on a 24-object plain-prompt diagnostic, not the main endpoint. b Explicitly specifying color suppresses shift across five recipes. c Conventional metrics explain little object-level color shift. Bands denote 95% cell-bootstrap intervals.

Figure 5: Reference-guided offline calibration. a DefaultShift-Select approaches a candidate-pool oracle as the budget grows. b Improvements remain positive under an independent evaluator and human labels.

Estimator	Null bias	Type I error	Rank $\rho$
Raw TV	0.197	0.980	0.991
$\mathrm{TV}_{\mathrm{adj}}$	0.008	0.560	0.996
Signed cross-fit	0.001	0.052	0.981
Dirichlet posterior	0.034	0.071	0.989

Table 2: Estimator simulation at

q=64

. Null calibration and alternative ranking are computed from frozen multinomial families.

Attribute	Clear $n$	Cov.	Acc.	Macro-F1	Fleiss $\kappa$
Color	261	0.87	0.91	0.91	0.71
Background	246	0.82	0.87	0.87	0.64

Table 3: Independent human validation. Each attribute is judged on 300 images. Clear

n

excludes abstentions, while coverage retains them in the denominator. Accuracy and macro-F1 compare Qwen with majority vote.

Figure 6: Independent human labels reproduce recipe separation. VLM circles and human diamonds show 95% intervals. Ordering is compared because label granularity differs.

Training source	Accuracy	Worst group
Reference	78.4 [76.9, 79.8]	70.1 [67.2, 73.0]
Replacement	72.1 [70.5, 73.6]	59.8 [56.5, 63.1]
Quality-only	72.6	61.2
DefaultShift-Select	76.4 [74.9, 77.9]	67.3 [64.2, 70.3]
Teacher-first hybrid	76.9	68.2

Table 4: Downstream classifier under balanced evaluation. Intervals are bootstrap 95% intervals.

Main claim	Supporting evidence	Supplement location
Recipe-dependent semantic default shift	Unified $q=64$ results, multi-attribute profile, lineage-aware comparisons	Sections C and C.2
Finite-sample reliability	Query convergence, four categorical distances, signed cross-fit, simulation, unknown-label sensitivity	Section D
Evaluator validity	Two human audit cohorts, confusion matrices, per-class scores, agreement, source-stratified tests	Section F
Bounded relation to diversity degradation	GRADE-style entropy comparison, entropy-preserved subsets, teacher-first hybrids	Section G
Recipe-specific mechanisms	Signed color fingerprints, CFG alignment, entropy and peak-mode probes	Section H
Actionability	Shared-pool baselines, independent relabeling, quality noninferiority, cost, downstream classification	Sections I and J

Table 5: Evidence map for the claims in the main paper. Every supplementary entry uses the same terminology and numerical convention as the main text.

Reference family	Replacement	Resolution	Steps	CFG or recipe setting
SDXL	NitroFusion-Realism-4step	1024	30 to 4	5 to official recipe
SDXL	Turbo	512	30 to 4	5 to 0
SDXL	DMD2	1024	30 to 4	5 to 0, LCM scheduler
SDXL	NitroFusion-Realism-1step	1024	30 to 1	5 to official recipe
SDXL	Flash-SDXL	1024	30 to 4	5 to 0, LCM scheduler
SDXL	Lightning	768	30 to 4	5 to official recipe

Table 6: Generation configurations for the 14 reference and replacement pairs. Entries in the final column report reference to replacement settings.

Replacement pair	$q=32$	$q=64$	Raw TV	Cross-fit	Unknown	95% CI
SDXL to NitroFusion-Realism-4step	0.269	0.303	0.454	0.271	0.031	[0.252, 0.354]
SDXL to Turbo	0.226	0.254	0.402	0.228	0.028	[0.215, 0.293]
SDXL to DMD2	0.210	0.235	0.384	0.209	0.026	[0.196, 0.273]
SDXL to NitroFusion-Realism-1step	0.205	0.228	0.378	0.203	0.030	[0.184, 0.273]
FLUX.1-dev to FLUX.1-schnell	0.192	0.203	0.346	0.181	0.021	[0.169, 0.239]
FLUX.1-dev to SwD-FLUX	0.171	0.187	0.331	0.166	0.023	[0.150, 0.225]

Table 7: Complete unified color benchmark. The

q=32

and

q=64

columns use identical cells. Confidence intervals, raw TV, signed cross-fit TV, and unknown rates correspond to the

q=64

endpoint.

Pair	Color	Background	Lighting	Viewpoint
NitroFusion-4	0.303 [0.252, 0.354]	0.115 [0.087, 0.145]	0.090 [0.066, 0.116]	0.030 [0.014, 0.047]
Turbo	0.254 [0.215, 0.293]	0.100 [0.078, 0.123]	0.080 [0.058, 0.103]	0.028 [0.013, 0.044]
DMD2	0.235 [0.196, 0.273]	0.086 [0.066, 0.108]	0.066 [0.046, 0.087]	0.022 [0.008, 0.037]
FLUX-schnell	0.203 [0.169, 0.239]	0.060 [0.038, 0.083]	0.050 [0.031, 0.070]	0.018 [0.004, 0.033]
Lightning	0.060 [0.044, 0.077]	0.030 [0.018, 0.043]	0.022 [0.010, 0.035]	0.009 [-0.003, 0.021]
LCM	0.054 [0.040, 0.069]	0.028 [0.015, 0.041]	0.024 [0.011, 0.038]	0.008 [-0.004, 0.020]

Table 8: Confirmatory

q=64

results across four semantic attributes. Every entry reports

\mathrm{TV}_{\mathrm{adj}}

followed by its 95% object-cluster interval.

Figure 7: A concrete

q=64

semantic marginal for the exact prompt a photo of a car . Both FLUX models produce plausible cars, while the reference and accelerated color histograms differ in category identity and probability mass. This cell-level view complements the aggregate benchmark.

Comparison	$\mathrm{TV}_{\mathrm{adj}}$	95% CI
SDXL to DMD2-4step	0.249	[0.221, 0.276]
DMD2-4step to NitroFusion-4step	0.163	[0.141, 0.187]
SDXL to NitroFusion-4step	0.269	[0.239, 0.298]
DMD2-4step to NitroFusion-1step	0.170	[0.146, 0.195]
SDXL to NitroFusion-1step	0.206	[0.182, 0.231]

Table 9: Lineage-aware NitroFusion analysis over 192 cells at

q=32

Figure 8: Supplementary robustness summary. a The 24-object plain-prompt diagnostic supports

q=64

for confirmatory estimation. b Explicitly naming color sharply reduces drift. c Aggressive-minus-mild separation persists across five prompt suites. d Recipe rankings remain stable across distribution distances and signed cross-fit TV. Error bars and bands give 95% intervals.

$q$	Turbo	LCM	Image slots
8	0.257 [0.188, 0.321]	0.032 [-0.019, 0.080]	576
16	0.279 [0.213, 0.349]	0.031 [-0.008, 0.075]	1,152
24	0.301 [0.246, 0.354]	0.049 [0.014, 0.084]	1,728
32	0.302 [0.243, 0.358]	0.052 [0.018, 0.087]	2,304
48	0.315 [0.261, 0.370]	0.046 [0.016, 0.079]	3,456
64	0.330 [0.277, 0.382]	0.038 [0.011, 0.066]	4,608

Table 10: Prefix-seed convergence on 24 plain-prompt objects. Brackets give 95% cell-bootstrap intervals.

Estimator	Null bias	Type I	Coverage	Alt. bias	Rank $\rho$
Raw empirical TV	0.197	0.980	0.000	+0.071	0.991
$\mathrm{TV}_{\mathrm{adj}}$	0.008	0.560	0.440	-0.034	0.996
Signed cross-fit	0.001	0.052	0.947	-0.029	0.981
Dirichlet shrinkage	0.034	0.071	0.918	+0.004	0.989
Permutation test	n/a	0.049	n/a	n/a	n/a
Multinomial LRT	n/a	0.058	n/a	n/a	n/a

Table 11: Estimator operating characteristics at

q=64

$q$	Type I	Coverage	Power
8	0.094	0.881	0.312
16	0.071	0.912	0.518
32	0.058	0.934	0.746
64	0.052	0.947	0.921
96	0.050	0.951	0.968

Table 12: Signed cross-fit calibration and power across query budgets for a true TV of

0.15

Pair	Primary	Unknown class	Coverage adjusted	Ref. unknown	Repl. unknown	95% CI
NitroFusion-4	0.115	0.190	0.125	0.100	0.060	[0.087, 0.145]
Turbo	0.100	0.185	0.113	0.125	0.100	[0.078, 0.123]
DMD2	0.086	0.176	0.095	0.125	0.055	[0.066, 0.108]
FLUX-schnell	0.060	0.090	0.062	0.024	0.037	[0.038, 0.083]
Lightning	0.030	0.095	0.034	0.125	0.095	[0.018, 0.043]
LCM	0.028	0.115	0.031	0.125	0.073	[0.015, 0.041]

Table 13: Background estimates under three treatments of unknown labels.

Suite	Aggressive	Mild	Difference	Explicit rate	Coverage	95% CI
Short underspecified photo	0.231	0.058	0.173	0.04	0.91	[0.138, 0.209]
Long compositional	0.152	0.049	0.103	0.38	0.88	[0.071, 0.136]
Style prompts	0.188	0.052	0.136	0.11	0.86	[0.101, 0.172]
Multilingual	0.204	0.061	0.143	0.07	0.79	[0.104, 0.183]
Real-user prompts	0.179	0.054	0.125	0.19	0.84	[0.090, 0.161]

Table 14: Prompt-suite stress tests. Differences compare aggressive and mild recipe groups.

Recipe	Unspecified	Specified	Reduction	Specified 95% CI
Turbo	0.284	0.065	77.2%	[0.037, 0.097]
DMD2	0.287	0.029	89.7%	[0.006, 0.060]
FLUX	0.254	0.048	80.9%	[0.016, 0.097]
LCM	0.055	0.011	80.2%	[0.002, 0.021]
TDM-SD3	0.167	0.044	73.8%	[-0.002, 0.112]

Table 15: Matched plain-prompt negative controls over 20 prompts and 32 seeds. The specified prompt inserts only the frozen color token.

Figure 9: Complete clear-case confusion matrices. Rows are human majority labels and columns are VLM predictions. Remaining errors concentrate among nearby color groups and between urban, nature, and abstract backgrounds.

Attribute	Class	Support	Precision	Recall	F1	Attribute	Class	Support	Precision	Recall	F1
Color	white	42	0.93	0.93	0.93	Background	plain or studio	58	0.87	0.90	0.88
Color	black	33	0.91	0.91	0.91	Background	indoor	52	0.94	0.87	0.90
Color	gray	38	0.85	0.87	0.86	Background	outdoor nature	54	0.89	0.89	0.89
Color	blue	36	0.97	0.94	0.96	Background	outdoor urban	44	0.80	0.84	0.82
Color	red	28	0.93	0.89	0.91	Background	abstract pattern	38	0.84	0.84	0.84
Color	green	24	0.96	0.96	0.96	Background	macro average	246	0.87	0.87	0.87

Table 16: Per-class VLM performance against human majority vote.

Statistic	Color	Background
Fleiss $\kappa$ , all labels	0.71 [0.65, 0.77]	0.64 [0.57, 0.71]
Fleiss $\kappa$ , clear cases	0.76	0.70
Fleiss $\kappa$ , coarse groups	0.81	0.75
Krippendorff $\alpha$	0.70	0.63
Mean pairwise agreement	0.83	0.78
Three-of-three agreement	0.62	0.54

Table 17: Inter-rater agreement. Confidence intervals refer to Fleiss

\kappa

with all legal labels retained.

Attribute	Quantity	Estimate	Difference	95% CI
Color	Reference split-half floor	0.16	n/a	[0.11, 0.21]
Color	Raw reference to Turbo	0.34	n/a	[0.26, 0.42]
Color	Raw reference to LCM	0.26	n/a	[0.19, 0.34]
Color	Bias-reduced reference to Turbo	0.18	n/a	[0.09, 0.27]
Color	Bias-reduced reference to LCM	0.10	n/a	[0.02, 0.19]
Color	Turbo minus LCM	n/a	0.08	[0.03, 0.13]

Table 18: Human grouped-label distribution distances with image-level paired bootstrap.

Pair	VLM $q=64$	Human	Human minus VLM	Human 95% CI	Direction
NitroFusion-4	0.303	0.246	-0.057	[0.158, 0.334]	agrees
Turbo	0.254	0.208	-0.046	[0.124, 0.293]	agrees
DMD2	0.235	0.193	-0.042	[0.108, 0.279]	agrees
FLUX-schnell	0.203	0.168	-0.035	[0.082, 0.254]	agrees
LCM	0.054	0.051	-0.003	[-0.030, 0.133]	mild pair swaps
Lightning	0.060	0.047	-0.013	[-0.034, 0.129]	mild pair swaps

Table 19: Human reproduction of the six-pair ranking. Human labels use five grouped color categories.

Recipe	$\Delta$ Gray	$\Delta$ Warm	Color $\mathrm{TV}_{\mathrm{adj}}$
DMD2	-0.124	+0.159	0.235
Turbo	-0.069	+0.082	0.254
LCM	-0.044	+0.046	0.054
Lightning	-0.038	+0.048	0.060
FLUX-schnell	+0.019	-0.070	0.203

Table 20: Signed color fingerprints. Changes are replacement minus reference probability mass.

Panel of Figure 10 — Figure 10: Selected and excluded candidates for the prompt a photo of a helicopter . Reference samples define the target. Real FLUX.1-schnell candidates are separated into selected, quality-eligible but unselected, and below-floor outcomes. Selection changes raw TV from 0.406 to 0.000 for this object. Aggregate evidence uses the held-out 24-object evaluation in Table 21 .

Method	Turbo	DMD2	FLUX
Random	0.0%	0.0%	0.0%
Quality-only	1.2%	0.9%	3.1%
DINO k-center	4.8%	5.3%	8.4%
Semantic entropy	7.9%	7.2%	15.3%
Rejection sampling	10.4%	11.8%	29.6%
Quota sampling	12.7%	15.1%	38.9%

Table 21: Cross-fit TV reduction from the shared fourfold candidate pool.

Recipe	Primary VLM	Second VLM	Human	Human 95% CI
Turbo	14.8%	12.1%	10.3%	[2.9, 17.8]
DMD2	16.8%	13.9%	11.7%	[3.8, 20.6]
FLUX	44.8%	38.2%	35.1%	[24.3, 46.2]

Table 22: Select reduction under independent evaluators and human labels.

Metric	Margin	Change	95% CI	Result
CLIP score	-0.010	+0.001	[-0.003, 0.005]	passes
Aesthetic score	-0.100	-0.015	[-0.070, 0.040]	passes
PickScore	-0.010	0.000	[-0.004, 0.004]	passes
Human quality preference	-0.050	-0.010	[-0.040, 0.020]	passes
Within-color DINO diversity	-5%	-2.1%	[-4.3, 0.8]	passes
Within-color Vendi	-5%	-1.8%	[-4.0, 1.1]	passes

Table 23: Quality noninferiority. A result passes when the interval lower bound remains above the frozen negative margin.

Training source	Balanced accuracy	95% CI	Worst-color accuracy	95% CI	ECE
Reference	78.4%	[76.9, 79.8]	70.1%	[67.2, 73.0]	0.061
Replacement random	72.1%	[70.5, 73.6]	59.8%	[56.5, 63.1]	0.094
Quality-only	72.6%	[71.0, 74.1]	61.2%	[57.9, 64.4]	0.089
DefaultShift-Select	76.4%	[74.9, 77.9]	67.3%	[64.2, 70.3]	0.071
Teacher-first hybrid	76.9%	[75.4, 78.3]	68.2%	[65.2, 71.1]	0.068

Table 24: Complete downstream results under balanced evaluation.

Figure 12: Illustrative reference and accelerated outputs for bird, cat, and dog prompts. Each row contains six experimental images. Samples are displayed to make semantic concentration visually inspectable. Quantitative conclusions use the complete frozen label banks rather than this curated subset.

Role	Identifier	Cached revision
Primary VLM	Qwen/Qwen2.5-VL-3B-Instruct	66285546d2b821cf421d4f5eb2576359d3770cd3
SDXL reference	stabilityai/stable-diffusion-xl-base-1.0	462165984030d82259a11f4367a4eed129e94a7b
Turbo	stabilityai/sdxl-turbo	71153311d3dbb46851df1931d3ca6e939de83304
LCM adapter	latent-consistency/lcm-lora-sdxl	a18548dd4956b174ec5b0d78d340c8dae0a129cd
DMD2 adapter	tianweiy/DMD2	be22767697a1f3ca656b73c776e15fa335c86c6c
FLUX reference	black-forest-labs/FLUX.1-dev	3de623fc3c33e44ffbe2bad470d0f45bccf2eb21

Table 25: Principal model identifiers and cached revisions.

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