Large-Scale Universal Defect Generation: Foundation Models and Datasets

Yuanting Fan; Jun Liu; Bin-Bin Gao; Xiaochen Chen; Yuhuan Lin; Zhewei Dai; Jiawei Zhan; Chengjie Wang

cs.CVApr 10, 2026

Large-Scale Universal Defect Generation: Foundation Models and Datasets

Authors: Yuanting Fan, Jun Liu, Bin-Bin Gao, Xiaochen Chen, Yuhuan Lin, Zhewei Dai, Jiawei Zhan, Chengjie Wang

Organizations: Tencent Youtu Lab, Shenzhen, China

Abstract

Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data. This issue is aggravated by substantial variations in defect scale and morphology, resulting in limited generalization, degraded realism, and category consistency. We address these challenges by introducing UDG, a large-scale dataset of 300K normal-abnormal-mask-caption quadruplets spanning diverse domains, and by presenting UniDG, a universal defect generation foundation model that supports both reference-based defect generation and text instruction-based defect editing without per-category fine-tuning. UniDG performs Defect-Context Editing via adaptive defect cropping and structured diptych input format, and fuses reference and target conditions through MM-DiT multimodal attention. A two-stage training strategy, Diversity-SFT followed by Consistency-RFT, further improves diversity while enhancing realism and reference consistency. Extensive experiments on MVTec-AD and VisA show that UniDG outperforms prior few-shot anomaly generation and image insertion/editing baselines in synthesis quality and downstream single- and multi-class anomaly detection/localization. Code will be available at https://github.com/RetoFan233/UniDG.

Figures & tables

Figure 1: The framework of existing anomaly generation methods. The proposed UniDG maintains straightforward inference and fully open-sourced framework.

Figure 2: Overview of UDG Dataset. (a) The construction pipeline for generating normal-abnormal-mask-caption quadruplets. (b) The distribution across scenarios and the frequency of mapped defect categories.

Figure 3: Overall framework of UniDG. UniDG leverages MM-DiT with a Defect-Context Editing strategy to integrate reference defects into target regions. Diversity-SFT and Consistency-RFT further improve synthesis quality.

Cls.
Metric	Few-shot Learning Anomaly Generation				Training-Free Few-shot Image Insertion
Metric	AnoDiff	AnoGen	DualAnoDiff	SeaS	AnyDoor	InsertAny	UniDG-SFT	UniDG-RFT
Acc-C	68.4	61.4	64.0	59.1	61.8	63.6	74.9	77.4
Det.&Loc.
AUROC-I	97.9	97.0	94.9	96.2	80.3	95.3	98.0	98.4
AP-I	98.9	95.0	96.6	95.5	80.7	95.5	98.6	99.0

Table 1: Quantitative results for single-class anomaly classification, detection, localization, and synthesis quality on MVTec-AD dataset. Bold and underline represent optimal and sub-optimal average results, respectively. Training-Free Few-Shot means that no data from MVTec-AD datasets was used during training, and only single retrieved reference image used during inference for fair comparisons.

Few-shot Anomaly Generation
Methods	Binary Anomaly Detection Metrics					Semantic Segmentation Metrics
Methods	AUROC-I	AP-I	AUROC-P	AP-P	PRO-P	mIoU	BG-mIoU	FG-mIoU
AnoDiff	92.79	96.24	95.90	61.41	81.82 +0.0%	37.87 -4.7%	98.47	23.83 -9.9%
AnoGen	93.10	96.20	94.85	63.32	79.54 -2.8%	39.72 +0.0%	98.54	26.45 +0.0%
DualAnoDiff	91.00	95.40	95.31	63.19	79.96 -2.3%	39.43 -0.7%	98.57	26.27 -0.7%
SeaS	89.29	93.76	95.89	60.98	81.01 -1.0%	35.41 -10.9%	98.33	20.48 -22.6%

Table 2: Quantitative results for multi-class anomaly semantic segmentation on the MVTec-AD dataset.

Figure 4: Qualitative comparisons of anomaly generation performance on MVTec-AD dataset.

Figure 5: Qualitative comparisons of cross-object defect generation capabilities on MVTec-AD dataset.

Arch.	Single-class Metrics			Multi-class Metrics
Arch.	Acc	AP-I	AP-P	PRO-P	mIoU	FG-mIoU
Baseline	57.8	92.5	43.1	75.3	29.4	13.3
+ Diptych input	62.3	94.0	51.6	78.5	32.3	16.4
+ Normal Reg	64.9	94.9	56.7	82.1	34.9	22.1
+ Defect Attn	65.5	95.8	60.1	84.0	36.1	22.8
+ Diversity-SFT	74.9	98.6	77.0	91.0	41.8	28.5

Table 3: Ablation studies on the components within UniDG.

Figure 6: Comparison of performance between UniDG-SFT and UniDG-RFT using Good-Same-Bad (GSB) evaluation.

Data Proportion			Multi-class Metrics			Synthesis Metrics
(a)	(b)	(c)	PRO-P	mIoU	FG-mIoU	Cons	Qual
0	1	1	76.2	30.3	14.5	3.82	3.73
1	0	1	78.3	31.4	15.7	3.91	3.87
1	1	0	90.5	39.1	26.0	3.89	3.92
1	1	1	91.0	41.8	28.5	4.15	4.09
2	1	1	90.9	41.9	28.5	4.14	4.12

Table 4: Ablation studies on the training data allocation.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: The part visualizations of the abnormal-normal-mask-caption quadruplets of the UDG Dataset.

FLUX.1-Fill-dev
Component	Parameters	Enabled	Status
CLIP Text Encoder	$\sim$ 125M	/
T5 XXL Text Encoder	$\sim$ 4.7B	/
FLUX-VAE	$\sim$ 84M	/
MM-DiT	$\sim$ 12B	/	& LoRA
FLUX.1-Redux-dev

Appendix

Table 5: Model Structure and Parameters. Enabled means that this component is enabled in the reference defect-based synthesis or text instruction-based synthesis.

Figure 10: The quantitative comparison between the proposed UniDG-Text and existing proprietary advanced methods.

Figure 11: The failure cases of UniDG-SFT.

Figure 12: The system prompt for Defect-Und-Reward and MLLM-based comprehensive scores.

Accuracy	GPT-4.1	GPT-5	Gemini-3-Pro	Qwen3-VL			Defect-Und-Reward-4B	Defect-Und-Reward-8B
Accuracy	GPT-4.1	GPT-5	Gemini-3-Pro	4B	8B	32B	Defect-Und-Reward-4B	Defect-Und-Reward-8B
RefAd	84.2	82.6	86.4	74.6	72.4	80.0	82.4	84.8
Cons	81.6	83.8	88.8	75.2	78.4	79.8	82.0	86.0
Reas	86.4	88.4	87.6	62.8	68.0	73.2	84.8	85.4
Qual	92.0	92.4	94.8	84.2	86.8	87.4	91.6	93.4

Appendix

Table 6: Benchmark results on UDG-Reward-Bench, reporting the accuracy of prediction with advanced MLLMs on reference adherence, consistency, reasonability, overall quality.

Figure 13: The part visualizations of UDG-Reward-Bench.

Table 19

Model	Scale	Anomaly	Defect				Object		Average
Model	Scale	Discrimination	Classification	Localization	Description	Analysis	Classification	Analysis	Average
Random Chance	–	50.00	25.00	25.00	25.00	25.00	25.00	25.00	28.57
Human (expert)	–	95.24	75.00	92.31	83.33	94.20	86.11	80.37	86.65
Human (ordinary)	–	86.90	66.25	85.58	71.25	81.52	89.58	69.72	78.69
Claude-3.5-sonnet	–	60.14	60.14	48.81	67.13	79.11	85.19	79.83	68.36
Gemini-1.5-pro	–	68.63	60.12	58.56	70.38	82.46	89.20	82.25	73.09

Appendix

Table 9: The quadruplets of UDG improves MLLM-based anomaly classification and detection methods.

Method	AnoDiff	AnoGen	DualAnoDiff	SeaS	AnyDoor	InsertAny	UniDG
Paradigm	DDIM					Rectified Flow
Resolution	$256\times 256$	$256\times 256$	$512\times 512$	$512\times 512$	$512\times 512$	$768\times 768$	$768\times 768$
Iterations	50	50	100	25	50	50	28
Inference Time (s)	3.47	3.21	19.68	6.73	7.59	28.04	11.59

Appendix

Table 10: The Inference Time of different methods.

Coefficient		Single-class Metrics			Multi-class Metrics			Synthesis Metrics
$w_{gen}$	$w_{det}$	Acc	AP-I	AP-P	PRO-P	mIoU	FG-mIoU	Cons	Qual
0.0	1.0	78.3	99.1	78.1	91.3	45.3	32.5	4.01	3.97
0.2	0.8	77.6	99.0	77.6	91.2	44.7	32.2	4.15	4.17
0.5	0.5	77.4	99.0	77.3	91.1	44.2	31.9	4.21	4.24
0.8	0.2	76.7	98.8	76.7	91.0	43.7	30.7	4.34	4.39
1.0	0.0	71.6	97.3	75.8	88.3	39.8	27.1	4.59	4.67

Appendix

Table 11: Ablation studies on the different reward models during Consistency-RFT.

w_{gen}

and

w_{det}

denotes the coefficient of the Defect-Und-Reward and Defect-Recog-Reward models, respectively.

Method	Metric	Category															Average
Method	Metric	bottle	cable	capsule	carpet	grid	hazelnut	leather	metal_nut	pill	screw	tile	toothbrush	transistor	wood	zipper	Average
Few-shot Learning Anomaly Generation
AnoDiff	AUROC-I	100.0	99.8	91.3	93.2	97.5	99.6	99.9	100.0	99.1	88.0	100.0	100.0	99.8	100.0	100.0	97.9
	AP-I	100.0	99.4	97.4	95.4	99.0	99.6	100.0	100.0	98.9	93.6	100.0	100.0	99.6	100.0	100.0	98.9
	AUROC-P	99.1	82.2	97.7	97.2	91.8	99.7	99.8	99.6	99.8	93.6	99.1	98.1	94.1	98.3	99.4	96.6
	AP-P	87.5	82.2	34.5	78.6	30.3	95.5	77.5	98.2	96.7	37.2	92.9	60.3	94.1	78.4	87.3	75.4

Appendix

Table 12: Quantitative results for anomaly detection and localization on MVTec-AD dataset. Bold and underline represent optimal and sub-optimal average results, respectively.

Category	AnoDiff	AnoGen	DualAnoDiff	SeaS	AnyDoor	InsertAny	UniDG-SFT	UniDG-RFT
bottle	90.70	53.49	55.81	83.72	65.12	79.07	88.37	83.72
cable	67.19	60.94	48.44	50.00	62.50	76.56	79.69	87.88
capsule	66.67	24.00	49.33	30.67	65.33	41.33	68.00	73.33
carpet	58.06	40.32	69.35	41.94	53.23	77.42	64.52	72.58
grid	42.50	57.50	62.50	32.50	60.00	32.50	50.00	40.00
hazel nut	85.42	85.42	79.17	85.42	81.25	95.83	95.83	95.83

Appendix

Table 13: Quantitative results for anomaly classification on MVTec-AD dataset. The higher accuracy denotes the generated data is more consistent with the real data.

Method	Metric	Category															Average
Method	Metric	bottle	cable	capsule	carpet	grid	hazelnut	leather	metal_nut	pill	screw	tile	toothbrush	transistor	wood	zipper	Average
AnoDiff	IL	0.16	0.37	0.16	0.20	0.39	0.29	0.39	0.24	0.23	0.23	0.50	0.15	0.27	0.34	0.23	0.28
	IL-a	0.17	0.13	0.08	0.14	0.11	0.17	0.10	0.22	0.14	0.07	0.21	0.14	0.16	0.18	0.12	0.14
	RefAd	3.76	4.00	3.69	3.96	4.11	3.43	3.82	3.86	3.97	3.62	3.87	2.63	3.74	3.49	4.25	3.84
	Cons	3.80	3.93	4.13	4.29	3.62	3.54	4.21	3.92	4.16	3.75	4.13	2.99	4.01	3.82	4.00	3.97
	Reas	4.29	4.39	4.48	4.69	4.41	3.58	4.07	4.19	3.85	4.00	3.89	3.06	4.38	3.85	4.26	4.18

Appendix

Table 14: Comparison of synthesis data on IL, IL-a, Reference Adherence, Consistency, Reasonability, Quality on MVTec-AD dataset.

Method	Metric	Category												Average
Method	Metric	candle	capsules	cashew	chewinggum	fryum	macaroni1	macaroni2	pcb1	pcb2	pcb3	pcb4	pipe_fryum	Average
Few-shot Learning Anomaly Generation
SeaS	AUROC-I	73.70	78.20	85.90	97.00	90.40	81.30	63.80	96.80	88.00	91.50	98.10	71.30	84.67
	AP-I	71.60	82.70	90.50	98.10	93.60	70.70	52.10	95.90	89.00	88.40	94.80	75.50	83.58
	AUROC-P	84.10	98.10	98.30	98.80	96.90	98.10	91.40	97.60	94.70	90.50	95.60	99.10	95.27
	AP-P	1.60	50.70	83.60	77.80	67.10	7.60	0.10	86.70	21.40	38.30	49.50	78.70	46.93

Appendix

Table 15: Quantitative results for anomaly detection and localization on VisA dataset. Bold and underline represent optimal and sub-optimal average results, respectively.

Category	SeaS	UniDG-SFT	UniDG-RFT
candle	73.70	87.50	91.20
capsules	78.20	91.20	90.40
cashew	85.90	84.30	85.20
chewinggum	97.00	96.40	96.70
fryum	90.40	92.30	91.50
macaroni1	81.30	89.60	92.30

Appendix

Table 16: Quantitative results for anomaly classification on VisA dataset. The higher accuracy denotes the generated data is more consistent with the real data.

Few-shot Anomaly Generation
Methods	Binary Anomaly Detection Metrics					Semantic Segmentation Metrics
Methods	AUROC-I	AP-I	AUROC-P	AP-P	PRO-P	mIoU	BG-mIoU	FG-mIoU
SeaS	81.72	82.87	95.83	45.85	78.73	24.99	99.58	11.83
Training-Free Few-shot Image Insertion
UniDG-SFT	89.21	91.34	97.88	58.14	88.45	31.79	99.54	18.89
UniDG-RFT	91.37	92.96	97.59	63.20	90.92	36.23	99.55	23.47

Appendix

Table 17: Quantitative results for multi-class anomaly semantic segmentation on the VisA dataset.

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Anomaly generation is often framed as few-shot fine-tuning with anomalous samples, which contradicts the scarcity that motivates generation and tends to overfit category priors. We tackle the setting where no real anomaly samples or training are available. We propose Delta-Denoising (DeltaDeno), a training-free zero-shot anomaly generation method that localizes and edits defects by contrasting two diffusion branches driven by a minimal prompt pair under a shared schedule. By accumulating per-step denoising deltas into an image-specific localization map, we obtain a mask to guide the latent inpainting during later diffusion steps and preserve the surrounding context while generating realistic local defects. To improve stability and control, DeltaDeno performs token-level prompt refinement that aligns shared content and strengthens anomaly tokens, and applies a spatial attention bias restricted to anomaly tokens in the predicted region. Experiments on public datasets show that DeltaDeno achieves great generation, realism and consistent gains in downstream detection performance. Code will be made publicly available at https://github.com/CROVO1026/DeltaDeno.

Abstract

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