CorrFlow: Self-Correcting Flow Matching

Hangwei Zhang; Nathan Rouillé; Chuanxia Zheng

cs.CVOct 17, 2026

CorrFlow: Self-Correcting Flow Matching

Authors: Hangwei Zhang, Nathan Rouillé, Chuanxia Zheng

Organizations: PVG, Nanyang Technological University · Télécom Paris, Institut Polytechnique de Paris

Abstract

Flow-matching models generate samples through iterative velocity predictions, but typically do not revisit these predictions using the intermediate representations that produced them. We introduce CorrFlow, a self-correcting flow-matching framework that reuses this information through a lightweight refiner. Under rectified-flow interpolation, the error of the one-step clean prediction equals the realized velocity residual scaled by the noise level. Motivated by this identity, the refiner learns to predict residual corrections from the base model's clean prediction, conditioning, noise level and hidden states from the same forward pass. The correction is added to the base velocity before each sampling update, so subsequent predictions are made from the corrected state. This requires no additional base-model evaluations and only one lightweight refiner evaluation per step. Our theoretical analyses establish conditions under which feature reuse outperforms raw-input affine predictors and refiner training achieves greater residual-risk reduction than continued base-model training under a matched training compute budget. Experiments on class-conditional image generation, text-to-video generation and image-to-video generation show consistent improvements over the corresponding base models, including gains in visual quality and physical consistency. Project Page: https://fogradio.github.io/corrflow-project

Figures & tables

Figure 1: CorrFlow learns from its own imperfect predictions. The base model’s clean prediction (blue) deviates from the clean reference (orange arrow). The resulting correction shifts the prediction closer to the clean reference by $-\sigma\kappa\hat{\bm{r}}$ (green arrow), where $\sigma$ is the noise level and $\kappa$ controls the correction strength. The initial noise is obtained by adding Gaussian noise to the clean reference.

Figure 2: CorrFlow overview for video generation. (a) At noise level $\sigma$ , the refiner predicts a residual $\hat{\bm{r}}$ from the base DiT’s clean prediction $\hat{\mathbf{x}}_{0}=\mathbf{x}_{\sigma}-\sigma\bm{v}_{\bm{\theta}}$ , selected hidden states $\bm{h}_{L}$ , conditioning tokens $\bm{c}$ and noise-level embedding $\bm{e}_{\sigma}$ . Sampling corrects the base velocity as $\bm{v}^{\kappa}=\bm{v}_{\bm{\theta}}+\kappa\hat{\bm{r}}$ with strength $\kappa$ . (b) The refiner alternates self-attention over clean-prediction tokens with cross-attention to projected conditioning and fused hidden-state tokens. Noise conditioning modulates its blocks and sample-space output head, while 3D rotary embeddings encode video token coordinates. Frames show outputs and cubes denote sample tensors.

Method	Epochs	Guidance	IS $\uparrow$	Representation Fréchet Distance ( $\mathrm{FD}_{r}$ ) $\downarrow$
Method	Epochs	Guidance	IS $\uparrow$	ConvNeXt ( Liu et al., 2022 )	DINOv2 ( Oquab et al., 2023 )	CLIP ( Radford et al., 2021 )
RAEv2 ( Singh et al., 2026 )	160	No	233.9	1.28	2.22	7.18
SelfFlow ( Chefer et al., 2026 )	160	No	146.4	4.81	7.08	12.13
Ours	80+80	No	234.0	1.24	2.21	7.14
SiT-XL/2 ( Ma et al., 2024 )	800	Yes	256.7	2.02	7.89	17.69
RAE-XL ( Zheng et al., 2026a )	800	Yes	261.0	1.79	2.11	7.87

Table 1: Comparison on ImageNet-256 ( Deng et al., 2009 ) generation. Representation Fréchet Distance (

\mathrm{FD}_{r}

) is reported in three feature spaces.

Method	VideoPhy2						FinePhyEval
	ALL			HARD			Object	Action	Physics	Overall
	PC	SA	Joint	PC	SA	Joint	Object	Action	Physics	Overall
Wan 2.1-14B ( Wan et al., 2025 )	60.5	29.0	24.7	45.6	8.3	3.9	0.86	0.53	0.46	0.59
PhysRAG-5B ( Cheng et al., 2026 )	65.6	24.7	21.0	59.4	7.2	4.4	0.88	0.49	0.37	0.55
VideoREPA-5B ( Zhang et al., 2025c )	73.6	25.8	23.6	62.8	6.7	5.0	0.82	0.37	0.30	0.47
Wan 2.1-1.3B ( Wan et al., 2025 )	57.8	30.0	24.8	36.7	11.7	5.6	0.84	0.49	0.36	0.54

Table 2: Physics-consistent text-to-video generation on VideoPhy2 ( Bansal et al., 2026 ) and FinePhyEval ( Pothiraj et al., 2026 ) . Higher is better for all metrics.

Method	SC	TF	MS	AQ	IQ	OC	SR	TS	QS	Total
VideoREPA-5B ( Zhang et al., 2025c )	95.47	98.23	98.54	57.19	59.80	67.56	51.85	22.71	79.96	76.67
PhysRAG-5B ( Cheng et al., 2026 )	96.31	98.71	96.93	56.80	65.56	80.50	77.33	23.18	83.07	80.08
Wan 2.1-1.3B ( Wan et al., 2025 )	91.83	99.17	96.51	57.24	59.14	76.09	62.37	22.23	79.81	76.93
VideoJAM-1.3B ( Chefer et al., 2025 )	91.51	99.13	96.05	58.41	62.68	79.22	66.17	24.03	81.18	78.76
DreamWorld-1.3B ( Tan et al., 2026 )	93.62	98.81	98.07	59.59	66.76	81.32	70.47	23.82	83.49	80.97
VPT-1.3B ( Zheng et al., 2026b )	92.65	98.64	97.85	58.09	63.68	73.43	73.35	22.52	82.26	78.79

Table 3: VBench ( Huang et al., 2024 ) comparison on selected metrics (0–100, higher is better).

Figure 3: Qualitative comparison on text-to-video generation. Each scenario compares VideoREPA-5B, VPT-1.3B and CorrFlow (Ours) under the same text prompt, with frames sampled at $0\%$ , $25\%$ , $50\%$ , $75\%$ and $100\%$ of each generated video. The prompts are drawn from VBench (amusement park), FinePhyEval (a domino dropped into a mug) and VideoPhy2 (a somersault on a bouncy castle, a coin caught in a metal cup).

Method	Spatial	Spatio-temporal	Weighted Spatial	MSE Score	Verified Score
P-Video-19B ( Pruna AI, 2026 )	38.60	16.40	22.90	23.30	25.30
Wan2.2-5B ( Wan et al., 2025 )	35.89	26.44	22.96	25.54	27.71
WMReward-5B ( Yuan et al., 2026 )	36.47	22.38	19.79	27.26	26.48
VLIPP-5B ( Yang et al., 2025 )	40.51	19.09	23.66	26.48	27.43
PhysRAG-5B ( Cheng et al., 2026 )	36.46	22.07	20.22	23.41	25.54
CorrFlow (Ours)-5B	43.43	26.37	24.74	29.24	30.95

Table 4: Physics-IQ Verified ( Rädsch et al., 2026 ) results on the image-to-video track. All evaluations are without LLM usage. All scores are percentages, and higher is better for every metric.

Figure 4: Qualitative comparison on the Physics-IQ Verified image-to-video track. Each scenario compares WMReward-5B, PhysRAG-5B and CorrFlow (Ours), which share the Wan2.2-TI2V-5B base model and receive the same settings. Temporal order is from left to right. The left scenario shows a statuette’s shadow under a moving light, and the right scenario shows a knife slicing a mandarin.

Method	AQ	BC	IQ	OC	SC	QS
w/o time-step	51.5	92.4	63.7	58.5	91.2	79.7
w/o self-attention	48.9	92.1	60.4	58.1	90.0	78.5
w/o cross-attention	48.5	91.4	60.7	58.2	90.2	78.5
w/o hidden-state MLP	49.3	92.4	63.6	58.2	90.9	79.3
w/o refiner	51.3	92.5	62.0	59.1	91.3	78.6
$\bm{v}$ input	50.4	92.4	63.7	60.2	91.7	79.8

Table 5: Component ablations (%, higher is better). Colors rank the four-decimal scores in Table 10 , which also expands the metric names.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Image representation	Steps	Guidance
SiT-XL/2 ( Ma et al., 2024 )	SD-VAE	250	CFG
DDT-XL ( Wang et al., 2026b )	SD-VAE	250	CFG
SiT-XL/2-REPA ( Yu et al., 2025 )	SD-VAE	250	CFG
LightningDiT ( Yao et al., 2025 )	VA-VAE	250	CFG
REG ( Wu et al., 2026 )	SD-VAE	250	CFG
REPA-E ( Leng et al., 2025 )	Jointly tuned VAE	250	CFG

Appendix

Table 6: Evaluation configurations for all seven published image-generation baselines in Table 7 . Steps count sampler updates. CFG combines conditional and unconditional predictions, while AutoGuidance combines a main model with a weaker model.

Published baselines
Method	Epochs	Guidance	IS $\uparrow$	FID $\downarrow$	Representation Fréchet Distance ( $\mathrm{FD}_{r}$ ) $\downarrow$
Method	Epochs	Guidance	IS $\uparrow$	FID $\downarrow$	ConvNeXt	DINOv2	CLIP
SiT-XL/2 ( Ma et al., 2024 )	800	Yes	256.7	2.12	2.02	7.89	17.69
DDT-XL ( Wang et al., 2026b )	800	Yes	309.3	1.26	1.02	4.26	13.86
SiT-XL/2-REPA ( Yu et al., 2025 )	800	Yes	306.1	1.42	1.22	4.27	12.65
LightningDiT ( Yao et al., 2025 )	800	Yes	294.3	1.42	1.09	3.76	10.21

Appendix

Table 7: Comparison on ImageNet-256 ( Deng et al., 2009 ) generation. For published baselines, the FID column reports guided FID (gFID) as in the original papers. Models are evaluated with and without guidance as indicated. For our method,

80+40

and

80+80

indicate that we start from the 80-epoch RAEv2 checkpoint and train our refiner for an additional 40 and 80 epochs, respectively. “Ours (base only)” evaluates the corresponding base model without the refiner at inference and reports the total base-model training epochs. Representation Fréchet Distance (

\mathrm{FD}_{r}

) is reported in the feature spaces of ConvNeXt ( Liu et al., 2022 ) , DINOv2 ( Oquab et al., 2023 ) and CLIP ( Radford et al., 2021 ) .

Figure 5: Qualitative results on ImageNet at $256\times 256$ resolution. Columns show shoji, plate rack, hatchet and marmoset. Row groups show Self-Flow (Selfflow), RAEv2 and CorrFlow (Ours). Each method–category block contains four samples in a $2\times 2$ grid, giving 48 images in total. All three base DiTs are trained for 160 epochs. The CorrFlow refiner is trained for 80 epochs.

Figure 6: Qualitative results on ImageNet at $256\times 256$ resolution. Columns show axolotl, Papillon, Miniature Poodle and Komondor. Row groups show Self-Flow (Selfflow), RAEv2 and CorrFlow (Ours). Each method–category block contains four samples in a $2\times 2$ grid, giving 48 images in total. All three base DiTs are trained for 160 epochs. The CorrFlow refiner is trained for 80 epochs.

Method	Subject Consistency	Background Consistency	Temporal Flickering	Motion Smoothness	Dynamic Degree	Aesthetic Quality	Imaging Quality	Object Class	Multiple Objects	Human Action
VideoREPA-5B ( Zhang et al., 2025c )	95.47	95.10	98.23	98.54	47.22	57.19	59.80	67.56	40.32	83.00
PhysRAG-5B ( Cheng et al., 2026 )	96.31	95.66	98.71	96.93	81.94	56.80	65.56	80.50	75.00	63.00
Wan 2.1-1.3B ( Wan et al., 2025 )	91.83	94.71	99.17	96.51	65.00	57.24	59.14	76.09	53.66	74.60
VideoJAM-1.3B ( Chefer et al., 2025 )	91.51	96.01	99.13	96.05	73.88	58.41	62.68	79.22	59.66	79.20
DreamWorld-1.3B ( Tan et al., 2026 )	93.62	94.95	98.81	98.07	79.16	59.59	66.76	81.32	65.03	81.20
VPT-1.3B ( Zheng et al., 2026b )	92.65	97.27	98.64	97.85	70.83	58.09	63.68	73.43	51.92	74.80

Appendix

Table 8: VBench results (Part 1/2): consistency, motion and object-level metrics. All scores are reported on a 0–100 scale.

Method	Color	Spatial Relationship	Scene	Appearance Style	Temporal Style	Overall Consistency	Quality Score	Semantic Score	Overall Score
VideoREPA-5B ( Zhang et al., 2025c )	86.12	51.85	32.27	21.37	22.71	24.20	79.96	63.53	76.67
PhysRAG-5B ( Cheng et al., 2026 )	93.95	77.33	15.55	22.00	23.18	23.18	83.07	68.12	80.08
Wan 2.1-1.3B ( Wan et al., 2025 )	89.93	62.37	20.03	22.91	22.23	24.25	79.81	65.43	76.93
VideoJAM-1.3B ( Chefer et al., 2025 )	88.92	66.17	28.34	22.67	24.03	25.01	81.18	69.08	78.76
DreamWorld-1.3B ( Tan et al., 2026 )	92.61	70.47	29.71	22.14	23.82	24.87	83.49	70.89	80.97
VPT-1.3B ( Zheng et al., 2026b )	86.99	73.35	20.13	20.87	22.52	23.42	82.26	64.91	78.79

Appendix

Table 9: VBench results (Part 2/2): appearance, style and overall scores. All scores are reported on a 0–100 scale.

Figure 7: Qualitative results on text-to-video generation. The upper case shows a rubber duck among dominoes, and the lower case shows a skateboard and a surfboard. Within each case, rows show VideoREPA, VPT, PhysRAG and Ours from top to bottom. Each row contains five frames sampled at evenly spaced relative positions across the video, ordered from left to right.

Figure 8: Qualitative results on text-to-video generation. The upper case shows a canoeist paddling across a lake, and the lower case shows two jetskis racing side by side. Within each case, rows show VideoREPA, VPT, PhysRAG and Ours from top to bottom. Each row contains five frames sampled at evenly spaced relative positions across the video, ordered from left to right.

Figure 9: Qualitative results on text-to-video generation. The prompt describes a parasailer with an orange parachute being pulled across the ocean by a speedboat. Rows show VideoREPA, VPT, PhysRAG and Ours from top to bottom. Each row contains five frames sampled at evenly spaced relative positions across the video, ordered from left to right.

Figure 10: Additional image-to-video comparison on Physics-IQ Verified: paper falling into water. A grabber releases crumpled paper into a bowl of water. Rows show GT, WAN, VLIPP, WMReward, PhysRAG and CorrFlow (Ours). Each row contains five video frames in temporal order.

Figure 11: Additional image-to-video comparisons on Physics-IQ Verified: motion and reflections. The upper case shows a suspended tennis ball being lowered in front of a mirror, and the lower case shows a tennis ball rolling toward a kettlebell beside a mirror. Within each case, rows show GT, WAN, VLIPP, WMReward, PhysRAG and CorrFlow (Ours). Each row contains five video frames in temporal order.

Figure 12: Additional image-to-video comparisons on Physics-IQ Verified: ball motion. The upper case shows a bouncing tennis ball near a ramp, and the lower case shows a blue ball rolling from a table onto glass. Within each case, rows show GT, WAN, VLIPP, WMReward, PhysRAG and CorrFlow (Ours). Each row contains five video frames in temporal order. Red boxes highlight visible balls and adjacent reflections in the four baselines after the initial frame.

Method	Aesthetic Quality (AQ)	Background Consistency (BC)	Imaging Quality (IQ)	Overall Consistency (OC)	Subject Consistency (SC)	Quality Score (QS)
w/o time-step	0.5147	0.9236	0.6369	0.5850	0.9120	0.7970
w/o self-attention	0.4892	0.9213	0.6043	0.5812	0.9000	0.7854
w/o cross-attention	0.4848	0.9144	0.6066	0.5821	0.9022	0.7846
w/o hidden-state MLP	0.4928	0.9238	0.6359	0.5817	0.9086	0.7928
w/o refiner	0.5129	0.9249	0.6204	0.5914	0.9126	0.7863
$\bm{v}$ input	0.5038	0.9238	0.6368	0.6021	0.9174	0.7980

Appendix

Table 10: Component ablations at four-decimal precision (0–1, higher is better). Column abbreviations match Table 5 . Colors indicate the top three scores in each column.

Flow Matching

Flow matching learns continuous-time vector fields that transport simple noise distributions into data for efficient generative modeling.

See all 639 papers

Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
Oct 2, 2026Divya Jyoti Bajpai, Arun Verma, Manjesh Kumar HanawalEfficient Inference Flow Matching
Department of IEOR IIT Bombay, Mumbai, India · SMART Centre Singapore
Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models
Jul 13, 2026Xu Han, Jiajing Hu, Li-Ping LiuRectified Flow Image Generation
Department of Computer Science Tufts University
Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching
Oct 4, 2026Kuan-Hsun Tu, Hsuan-Chi Liu, Jia-Wei Liao +3Flow Matching Video Prediction
National Taiwan University

Oct 2, 2026J/K move · Enter open · S save

Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation

Divya Jyoti Bajpai, Arun Verma, Manjesh Kumar Hanawal

Department of IEOR IIT Bombay, Mumbai, India · SMART Centre Singapore

Efficient Inference Flow Matching Image Generation Flow-Based Generative Modeling

Open paper AskPDF arXiv

Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function evaluations. Existing acceleration methods reduce the number of function evaluations but often introduce additional training overhead, degrade quality, or fail to account for input-dependent variability. We propose COFLOW, an inference-time method that adaptively selects the step counts each generation based on the prompt features. Our context-aware COFLOW is trained online with an unsupervised reward that balances inference efficiency and generation fidelity. Our method is plug-and-play, requiring no retraining of the underlying generative model. It generalizes to image and video generation, achieving over 2.5x speedup while preserving perceptual and semantic quality. We further provide a theoretical analysis establishing an O(1/K) forward-Euler discretization error bound under standard regularity conditions.

Abstract

Flow Matching

Explore similar work

Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation

Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models

Learning Conditional Source Distribution via Flow Reversal for Temporal Flow Matching