MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing

Houyuan Chen; Fanzhou Wang; Xiangyu Fan; Songlin Yang; Zhitao Yang; Lvmin Zhang; Pei Yang; Hao Zhao

cs.CVOct 16, 2026

MTEdit: Packed Trajectories Beat Rollouts for Multi-Turn Image Editing

Authors: Houyuan Chen, Fanzhou Wang, Xiangyu Fan, Songlin Yang, Zhitao Yang, Lvmin Zhang, Pei Yang, Hao Zhao,

Organizations: SenseTime Research · HKUST MMLab · Stanford University · AIR, Tsinghua University

Abstract

Multi-turn image editing aims to modify an image according to a sequence of instructions, making it better aligned with human-AI collaborative creation than single-turn editing. However, constructing high-quality multi-turn training data remains challenging, as conventional sequential rollout accumulates errors and introduces visual-quality drift across editing turns. To address this challenge, we introduce MTEdit, a multi-turn image editing dataset built through a pipeline centered on packed trajectory canvas (PTC) generation. By jointly generating multiple editing states within a unified visual context, this pipeline reduces visual-quality drift across turns. Covering both step-wise and context-aware editing scenarios, MTEdit comprises 500K multi-turn image editing trajectories, with up to 8 turns and an average of 5.38 turns per trajectory, making it one of the most extensive datasets for multi-turn image editing. We further introduce MTEdit-Bench, a benchmark designed to evaluate both scenarios by assessing models' ability to follow editing instructions, maintain content consistency, resist visual-quality drift, and effectively use historical context across turns. Using MTEdit, we fine-tune Qwen-Image-Edit-Causal to obtain Qwen-Image-MTEdit. On MTEdit-Bench, we evaluate a broad range of closed-source and open-source image editing models, including Qwen-Image-MTEdit. Experimental results show that Qwen-Image-MTEdit achieves the best performance among the evaluated open-source models, demonstrating the effectiveness of MTEdit in improving models' multi-turn image editing capabilities.

Figures & tables

Figure 1: Overview of our approach. (a,b) Packed trajectory canvas (PTC) generation reduces visual-quality drift compared with sequential rollout. (c) MTEdit offers more trajectories and turns than existing datasets. (d) Fine-tuning on MTEdit improves multi-turn editing performance in instruction following (IF), content consistency (CC), and visual-quality stability (DRS).

Dataset	#Traj.	#Edits	Max T.	Avg T.	Step-wise	Context-aware	Construction
MagicBrush [ 69 ]	3K	8K	3	2.58	✓	✗	Human
SEED-Data-Edit [ 17 ]	21K	81K	6	3.82	✓	✗	Human
ImgEdit [ 64 ]	115K	314K	3	2.73	✗	✓	Inpainting
Pico-Banana-400K [ 43 ]	77K	306K	6	3.97	✓	✗	Sequential rollout
MTEdit (Ours)	500K	2.69M	8	5.38	✓	✓	Packed trajectory canvas

Table 1: Comparison of representative multi-turn image editing datasets. #Traj. and #Edits denote the numbers of editing trajectories and edit pairs; Max T. and Avg T. denote the maximum and average numbers of turns; Step-wise and Context-aware indicate whether the dataset supports the two multi-turn editing settings; Construction describes how the dataset is built.

Figure 2: Overview of our data construction pipeline. (a) Seed image curation, which selects source images with high resolution and aesthetic quality; (b) PTC generation, in which VLMs derive a PTC prompt from each source image to guide closed-source image models in generating the corresponding PTC; and (c) PTC to multi-turn conversion, which processes the generated canvases into multi-turn editing trajectories with turn-level instructions and target images.

Figure 3: Statistics of MTEdit. (a) Edit-type taxonomy, (b) multi-turn data distribution, and (c) instruction word cloud.

Figure 4: Qualitative results on the step-wise sub-bench. Our method achieves competitive editing performance and maintains visual quality without noticeable degradation across editing turns.

	Turn 1			Turn 2			Turn 3			Turn 4
Method	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$
Nano Banana Pro [ 20 ]	9.75	9.79	4.17	9.73	9.53	4.22	9.63	9.35	4.22	9.53	9.06	4.19
Nano Banana 2 [ 21 ]	9.65	9.58	4.19	9.45	9.21	4.21	9.28	8.85	4.23	9.31	8.53	4.20
GPT Image 2 [ 42 ]	9.02	8.72	4.22	8.66	8.62	4.23	8.65	8.07	4.22	8.62	7.82	4.20
Seedream 5.0 Lite [ 4 ]	9.73	9.60	4.27	9.81	9.08	4.29	9.66	8.57	4.26	9.34	7.69	4.19
VINCIE [ 44 ]	1.13	4.31	2.47	0.01	0.02	1.66	0.00	0.00	1.40	0.00	0.00	1.33

Table 2: Quantitative results on the step-wise sub-bench. Closed-source scores are shown in gray ; among open-source methods and our model, different cell colors refer to best and 2nd-best results. For all metrics, higher values are better.

Figure 5: Visual quality comparison at Turn 8. From left to right: the source image and outputs from models trained on data from PTC generation and sequential rollout. Enlarged crops show fewer artifacts and cleaner details with our pipeline.

	Turn 1			Turn 2			Turn 3			Turn 4
Method	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$
PTC	9.64	9.61	4.17	9.63	9.27	4.26	9.30	8.59	4.21	8.67	7.67	4.22
Sequential rollout	9.66	9.50	4.19	9.32	9.04	4.27	8.97	8.20	4.18	7.95	7.58	4.19
	Turn 5			Turn 6			Turn 7			Turn 8
Method	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$	$\mathrm{IF}$	$\mathrm{CC}$	$\mathrm{DRS}$
PTC	8.21	7.15	4.21	7.63	6.47	4.21	6.24	5.37	4.23	5.06	4.44	4.22

Table 3: Quantitative ablation results on the step-wise sub-bench. Method denotes the strategy used to construct the training data: PTC generation or sequential rollout. The best results are bolded . Higher values indicate better performance for all metrics.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Block causal attention and 3D interleaved RoPE for Qwen-Image-MTEdit. Image and text tokens are temporally interleaved as “ [Image] - [Text] - [Image] - ... ”.

Figure 7: Example of PTC generation used in the MTEdit construction pipeline.

Figure 8: Visual quality drift comparison. We compare our data with rollout-style results generated using Seedream 5.0 Lite/GPT Image 2/Nano Banana 2. Columns show results at Turns 1/4/8. The visual quality of rollout-style methods degrades noticeably starting from Turn 4 (e.g., spurious speckling appears on the ground near the car), whereas our results remain relatively stable.

Model	Method	Turn 1	Turn 2	Turn 3	Turn 4	Turn 5	Turn 6	Turn 7	Turn 8
Ours	PTC	4.28	4.30	4.30	4.29	4.29	4.29	4.28	4.27
Seedream 5.0 Lite [ 4 ]	Sequential rollout	4.30	4.10	4.16	3.95	3.88	3.70	3.65	3.51
Nano Banana 2 [ 21 ]	Sequential rollout	4.32	4.31	4.30	4.24	4.14	4.00	3.84	3.70
GPT Image 2 [ 42 ]	Sequential rollout	4.28	4.24	4.18	4.12	4.04	3.94	3.88	3.83

Appendix

Table 4: Quantitative comparison of visual-quality drift across data construction methods. PTC denotes packed trajectory canvas. We report the Drift Robustness Score (

\mathrm{DRS}

) at each editing turn. Higher values indicate better visual quality; the best score at each turn is bolded .

	Turn 1			Turn 2			Turn 3			Turn 4
Method	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$
Nano Banana 2 [ 21 ]	8.70	9.04	4.28	8.70	8.64	4.27	8.32	8.38	4.25	7.64	7.46	4.19
Nano Banana Pro [ 20 ]	9.02	9.28	4.28	8.42	8.62	4.28	7.98	8.22	4.24	8.02	7.78	4.19
GPT Image 2 [ 42 ]	9.36	9.36	4.29	9.28	9.42	4.28	8.30	8.84	4.25	7.82	7.72	4.23
Seedream 5.0 Lite [ 4 ]	9.88	9.88	4.37	9.34	9.66	4.37	8.94	9.54	4.32	8.68	9.18	4.28
VINCIE [ 44 ]	1.58	5.30	2.79	0.20	0.20	1.76	0.06	0.06	1.71	0.00	0.00	1.51

Appendix

Table 5: Quantitative results on the persistent-constraint sub-bench. Closed-source scores are shown in gray ; among open-source methods and our model, different cell colors indicate the best and 2nd-best results. For all metrics, higher values indicate better performance. DRS is reported as an absolute score at every turn. After a case first has IF or CC below 6, subsequent IF and CC scores are set to zero; the triggering turn is retained. DRS uses actual scores at every turn, and all means include all cases.

Figure 9: Qualitative results on the persistent-constraint sub-bench. Yellow boxes highlight representative editing errors.

	Turn 1			Turn 2			Turn 3			Turn 4
Method	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$
Nano Banana 2 [ 21 ]	9.36	9.92	4.12	9.28	9.52	4.13	9.22	9.14	4.12	8.70	8.40	4.06
Nano Banana Pro [ 20 ]	9.56	9.76	4.09	9.30	9.56	4.09	8.24	8.82	4.08	7.84	8.12	4.00
GPT Image 2 [ 42 ]	9.72	9.78	4.14	9.34	9.84	4.18	8.78	9.14	4.17	8.42	8.68	4.18
Seedream 5.0 Lite [ 4 ]	9.80	9.82	4.20	8.46	9.76	4.19	7.54	8.12	4.17	7.30	7.44	4.15
VINCIE [ 44 ]	3.00	5.86	2.67	0.86	1.00	1.75	0.44	0.40	1.49	0.20	0.20	1.42

Appendix

Table 6: Quantitative results on the textual-reference sub-bench. Closed-source scores are shown in gray ; among open-source methods and our model, different cell colors indicate the best and 2nd-best results. For all metrics, higher values indicate better performance. DRS is reported as an absolute score at every turn. After a case first has IF or CC below 6, subsequent IF and CC scores are set to zero; the triggering turn is retained. DRS uses actual scores at every turn, and all means include all cases.

Figure 10: Qualitative results for textual-reference multi-turn image editing. Red boxes highlight representative editing errors.

	Turn 1			Turn 2			Turn 3			Turn 4
Method	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$	$\mathrm{IF}^{\ast}$	$\mathrm{CC}^{\ast}$	$\mathrm{DRS}$
Nano Banana 2 [ 21 ]	9.68	9.78	4.25	8.24	9.58	4.20	7.70	7.82	4.18	6.38	6.50	4.13
Nano Banana Pro [ 20 ]	9.74	9.96	4.25	8.22	9.66	4.23	7.44	7.94	4.17	6.42	7.08	4.07
GPT Image 2 [ 42 ]	9.86	9.84	4.21	9.30	9.80	4.25	8.76	9.20	4.26	7.72	8.20	4.22
Seedream 5.0 Lite [ 4 ]	9.58	9.74	4.30	8.54	9.72	4.27	7.60	8.20	4.22	6.72	7.52	4.16
VINCIE [ 44 ]	2.92	6.30	2.80	1.16	1.30	1.78	0.30	0.32	1.59	0.08	0.10	1.49

Appendix

Table 7: Quantitative results on the short-instruction sub-bench. Closed-source scores are shown in gray ; among open-source methods and our model, different cell colors indicate the best and 2nd-best results. For all metrics, higher values indicate better performance. DRS is reported as an absolute score at every turn. After a case first has IF or CC below 6, subsequent IF and CC scores are set to zero; the triggering turn is retained. DRS uses actual scores at every turn, and all means include all cases.

Figure 11: Qualitative results on the short-instruction sub-bench. Later turns progressively lengthen the flower stems using concise, context-dependent instructions.

Figure 12: Representative examples from MTEdit. Rows show step-wise, persistent-constraint, textual-reference, and short-instruction editing trajectories.

Sub-bench	IF / IF ∗		CC / CC ∗		DRS
Sub-bench	$\rho\uparrow$	$\tau_{b}\uparrow$	$\rho\uparrow$	$\tau_{b}\uparrow$	$\rho\uparrow$	$\tau_{b}\uparrow$
Step-wise	0.83	0.71	0.85	0.75	0.92	0.83
Persistent-constraint	0.88	0.78	0.85	0.75	0.89	0.78
Textual-reference	0.92	0.82	0.87	0.71	0.83	0.75
Short-instruction	0.82	0.71	0.83	0.67	0.91	0.78

Appendix

Table 8: Alignment between MTEdit-Bench and human judgments. We report Spearman’s

\rho

and Kendall’s

\tau_{b}

; higher values indicate stronger agreement.

Figure 13: Failure case of PTC generation. Content from different panels may interfere with one another, requiring additional filtering during data construction.

Figure 14: Additional example of step-wise multi-turn image editing.

Figure 15: Additional example of step-wise multi-turn image editing.

Figure 16: Additional example of persistent-constraint multi-turn image editing.

Figure 17: Additional examples of textual-reference multi-turn image editing.

Figure 18: Additional examples of short-instruction multi-turn image editing.

Figure 19: Data examples for step-wise multi-turn image editing.

Figure 20: Data examples for persistent-constraint multi-turn image editing.

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Open paper AskPDF arXiv

Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users. However, existing methods often exhibit noticeable drift after only a few editing turns, manifesting in two aspects: 1) unstable visual quality, with accumulating artifacts and degraded details; and 2) inconsistent image content, with unintended changes to preserved regions and prior edits. To address these challenges, we propose MT Forcing, a novel training paradigm for multi-turn image editing with unified multimodal models (UMMs) based on autoregressive diffusion. Specifically, we propose Single-Turn Distribution Matching Distillation (Single-Turn DMD), which supervises student self-rollouts with a teacher that applies cumulative edits to the source image in a single turn, providing visual-quality guidance without recursive error accumulation. Additionally, we introduce Teacher-Forcing Guidance, which supervises history-dependent transitions on reference trajectories during warmup and as a regularizer during distillation, encouraging cross-turn content consistency beyond what per-turn distribution matching alone can ensure. Extensive experiments demonstrate that our approach significantly improves the multi-turn image editing capabilities of UMMs, yielding more stable visual quality and stronger cross-turn consistency.

Abstract

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