ChronoEdit: Towards Temporal Reasoning for In-Context Image Editing and World Simulation
Authors: Jay Zhangjie Wu
Xuanchi Ren
Tianchang Shen
Tianshi Cao
Kai He
Yifan Lu
Ruiyuan Gao
Enze Xie
Shiyi Lan
Jose M. Alvarez
Jun Gao
Sanja Fidler
Zian Wang
Huan Ling
Venue: ICLR 2026 Poster (2026)
Keywords: image editing
generative models
Primary area: generative models

Abstract: Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem. First, ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video. To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image–prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility. Project page for code and models: https://research.nvidia.com/labs/toronto-ai/chronoedit

Published as a conference paper at ICLR 2026
CHRONOEDIT: TOWARDS TEMPORAL REASONING FOR IMAGE EDITING AND WORLD SIMULATION
Jay Zhangjie Wu Xuanchi Ren 12.
Tianchang Shen
Tianshi Cao Kai He
Yifan Lu"
Ruiyuan Gao' Enze Xie
Shiyi Lan' Jose M. Alvarez!
Jun Gao
Sanja Fidler12 Zian Wang Huan Ling
NVIDIA University of Toronto
ABSTRACT
Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present Chro- noEdit, a framework that reframes image editing as video generation problem. First, ChronoEdit treats the input and edited images the first and last frames of a video, allowing to leverage large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency. Second, ChronoEdit introduces a temporal reasoming stage that explicitly performs editing at inference time. Under this setting, target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations. The reasoning tokens are then dropped after a few steps to avoid the high computa- tional cost of rendering full video. To validate ChronoEdit, Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-arr baselines in both visual fidelity and physical plausibility. Project page for code and models: https: / http///eeeareeeeeee nv www. labs torottooooooooooooooooooo al/ chronoedit
introduce PBench-
INTRODUCTION
Recent large-scale generative models have transformed the landscape of image editing, enabling purely text-driven image modifications that impact diverse domains such as social media, e-commerce, education, and creative arts (Xiao et al., 2025; Labs et al., 2025; Liu al., 2025; Yu al., 2025). Beyond these consumer applications, image editing also offers a critical capability for simulation- related applications, providing controllable mechanism simulate that are otherwise difficult to capture in real-world data. For example, editing can create long-tail events for autonomous driving, where unexpected objects enter the road (Gao al., 2024; Lu et al., 2024), or visualize the outcomes of a robot arm manipulating objects in cluttered scene. In these cases, editing goes beyond aesthetics and serves generate diverse training and evaluation data for perception, planning, and reasoning.
but safety-critical scenarios
central requirement in the context of image editing for simulation is physical consistency: edited results must preserve existing objects and their properties (e.g., color, geometry) while reflecting the intended change. Without this constraint, edited results risk misrepresenting the scene and compromising downstream systems. Existing editing models have explored character or object continuity using video data to curate pixel-level editing pairs (Deng et al., 2025; Xiao et al., 2025; Chen et al., 2025), yet data alone have failed to ensure physical consistency. As illustrated in Fig. these models often hallucinate new objects or alter geometry in unintended ways. Such failures stem partly from architectural limitations: current approaches purely data-driven and lack mechanisms
Equal contribution; Corresponding author

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Published as a conference paper at ICLR 2026
(a) Reference Image
(h) "Change to a confident pose"
"Pick up the dragonfriit"
"The robot driving car"
(c) Reference Image
"Replace the black sedan with a red SUVV"
"Add jaywalker, front light on"
"Change traffic light red"
(i) Reference Image
ij "Superheroo stance, with its front paws ruised"
Chrono!
"The "Chrona!"
Transform high and PVC scale figure"
Figure Physical consistent image editing results with ChronoEdit-14B, ChronoEdit produces edits that are both visually convincing and physically consistent with the underlying scene context.
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Reference Image
Qwen-Image
Gemini2.5-Image
Ours
Failure cases of state-of-the-art image editing models. Current state-of-the-art models often struggle to maintain physical consistency on world simulation-related editing tasks. They may hallucinate unintended objects or distort scene geometry. In contrast, our method produces edits that are faithful to the instruction and remain coherent with the scene. Prompts (from top bottom): (1) "The left silver SUV makes U-turn", (2) "Pick up the spoon with the robot arm", and (3) "Close the wooden piece by hand".
Figure 2:
to enforce continuity, leaving them vulnerable to drifting edits that appear plausible but violate physical constraints.
Large-scale video generative models (Wan, 2025; Cosmos, 2025) have recently demonstrated strong capabilities to preserve object structure and coherence across consecutive frames. This inherent temporal prior makes them particularly well-suited for editing tasks that demand physical consistency. Building upon this insight, we introduce ChronoEdit, a foundation model for image editing explicitly designed preserve physical consistency. ChronoEdit repurposes pretrained video generative models for editing by reframing the task as a two-frame video generation problem, where the input image and its edited version are modeled as consecutive frames. When fine-tuned with curated image-editing data, this two- -wo-frame formulation equips the video model with editing functionalities while leveraging its pretrained temporal prior to preserve object fidelity.
For world simulation tasks (e.g., action editing) that demand stronger temporal coherence, we further introduce explicitly guided editing through temporal reasoning. Given an input image and an editing instruction, the model imagines and denoises a short video trajectory that realizes the edit while preserving temporal alignment with the input frame. The intermediate vidco frames in this trajectory act reasoning tokens, planning how the edit should unfold and thereby producing more physically plausible results (See Fig. 2). Beyond improving plausibility, simulating these intermediate frames also unveils the "thinking process" of the editing model, offering more interpretable view of how edits are constructed. To balance these benefits with efficiency, ChronoEdit can perform temporal reasoning during only the first few high-noise denoising steps. After that stage, the intermediate frames are discarded, and only the final frame of the trajectory is refined into the edited image.
Public benchmarks for image editing mainly target visual fidelity and instruction following, but rarely evaluate physical consistency. To address this gap, we introduce a new benchmark named PBench-Edit. This benchmark is constructed by carefully curating a collection of images paired with editing prompts that capture both real-world editing requirements and a broad spectrum of editing types. PBench-Edi is designed evaluate not only general- -urppse edits but also tasks that require physical and temporal consistency. Our experiments on PBench-Fdi demonstrate that ChronoEdit achieves state-of-the-an results, surpassing existing open-source baselines by a significant margin and narrowing the gap with leading proprictary systems.
In summary, we make the following contributions: (i) We propose ChronoEdit, foundation model for image editing designed to preserve physical consistency, (ii) We present effective design that turns a pretrained video generative model into an image editing model. (iii) We develop a novel temporal reasoning inference stage that further enforces physical consistency. (iv) We demonstrate that ChronoEdit achieves state-of- the-art performance among open-source models and competitive with leading proprietary systems. (v) We present benchmark tailored world simulation applications.
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Intermediate Editing Trajectory Visualization
Drop
Drop
Clean
Reference
Reference Image
Image
Noisy
Denoising
Rensoning
Tokens
Noisy Target
Editing Gemeration Stuge
Temporal
Denoised Target
Rensoning Stuge
From right left, the denoising process begins in the temporal reasoning stage, where the model imagines and denoises short trajectory intermediate frames. These intermediate frames act reasoning tokens, guiding how the should unfold in a physically consistent manner. For efficiency, the reasoning tokens are discarded in the subsequent editing frame generation stage, where the target frame is further refined into the final edited image.
Figure 3: Overview of the ChronoEdit pipeline.
RELATED WORK
Recent advances in image editing have been driven by large-scale foundation models. FLUX.1 Kontext (Labs et al., 2025) achieves strong instruction aligmment and multi-turn editing through billion-scale parameterization, while OmniGen (Xiao al., 2025) unifies text-to-image, editing, and subject-driven generation within single diffusion framework. Oween-Imae-Fddee (Wu et al., 2025a) extends a vision-language model with double-stream architecture for precise, high-fidelity edits. Proprictary systems such GPT-4o (OpenAI, 2025) and Gemini 2.5 Flash Image (Google, 2025) demonstrate robust multi-turn editing and conversational refinement at scale.
Prior work also explored leveraging video priors for image editing: Bagel (Deng et al., 2025), UniReal (Chen et al., 2025), and OmniGen (Xiao et al., 2025) use video-derived key frames to create temporally coherent image pairs. In complementary direction, Rotstein et al. (2025) is training free method that uses a pretrained image-to-video diffusion model to synthesize a sequence of intermediate frames, and then selects the frame that best satisfies the edit. Concurrent work (Wiedemer al., 2025) shows that strong video models like Veo 3 better preserve fine details and textures during edits.
A complete discussion of related work can be found in Appendix A.
CHRONOEDIT
In this section, first provide background the rectified flow model for video generation in Sec. 3.1. Next, we outline our core design in Sec. 3.2, which adapts pretrained image-to-video model for image editing, and detail our training with video reasoning tokens. We then describe the inference procedure in Sec. 3.3, highlighting how video reasoning enhances consistency. Finally, Sec. 3.4 describes the post-training process of ChronoEdit, which improves inference speed. An overview of the full architecture is shown in Fig. 3.
3.1 BACKGROUND: RECTIFIED FL.W FOR VIDEO GENERATIVE MODELS
Modern video generative models typically rely on pretrained variational autoencoder (VAE) (Blattaan et al., 2023b;a; Kong et al., 2024; Gupta al., 2024; Cosmos, 2025; Wan, 2025) that compresses raw videos x E Fx3x into a compact latent representation Zo = E(x) E
I xCx Training and inference operate in this latent space, and the decoder reconstructs videos as x Dzz) To handle temporal structure, causal video VAEs encode the first frame independently and compress subsequent chunks conditioned on past latents. In our work, we adopt the Wan2.1
VAE (Wan, 2025), which yields F' (F-1) + 1, C 16, h H/8, and W/8.
Rectified flow (Liu et al., 2022; Albergo & Handen-Eijnded 2022; Lipman et al., 2022: Esser et al., 2024a) provides principled framework for training video generators via flow matching. Given
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video data x Pdata and Gaussian noise (0, I), the interpolated latent at timestep t e [0. is defined as 2, = (1 t) t)zo + te, with Zo = {x)) A denoiser Fø Ez( t; c) with parameters is
trained to predict the target velocity field (E Zo) by minimizing:
Ce Be-p(t), x- (o0.)) [Fo(z,, (;y, ( zo 20)]
Here y denotes optional text conditioning and is optional image conditioning.
3.2 RE-PURPOSING VIDEO GENERATIVE MODELS FOR EDITING
Formally, the image editing task aims to transform a reference image e into an output image p that satisfies a natural-language instruction y. Our key insight is to repurpose a pretrained image-to-video model for this task, leveraging its inherent temporal priors to maintain consistency between the source and target images.
Encoding Editing Pairs. To leverage temporal priors in pretrained video models, we reinterpret the editing pair {c, p) as a short video sequence. Specifically, the input image is encoded as the first latent frame Ze = E(c), while the output image is repeated four times to match the video VAE's 4x temporal compression and encoded as Zp ((repett (p. 4)). This produces two temporal latents aligned with the video model's architecture. We additionally adjust the model's 's 3D-ffctorized Rotary Position Embedding (RoPE) (Su et al., 2024) by anchoring input image e at timestep 0 and output image p at predefined timestep T, explicitly encoding their temporal separation. For convenience, we fix T' to the length of the joint-training video latents (see following section).
Temporal Reasoning Tokens. To go beyond direct onput-output mapping, we explicitly model the transition between input image e and output image p. The goal is to encourage the model to imagine plausible trajectory rather than regenerate the target image in single step, which often leads to abrupt changes. By reasoning through intermediate states, the model better preserves object identity, geometry, and physical coherence. In practice, we insert intermediate latent frames between ze and 2p- These frames are initialized with random noise and denoised jointly with the output frame latents. We refer to them as temporal reasoning tokens r, since they act as intermediate guidance that help the model "think" " through plausible transitions.
Unifying Image Pairs and Videos. Similarly the video denoiser introduced in Sec. 3.1, we define the mageeediiing denoiser as Fo(Zp.t:te y, ze), where Zp and are the flow variables. Our formulation naturally supports training on both image-editing pairs and full video sequences within a unified framework. For public image-editing datasets, each pair (c. p) y) is reinterpreted two-frame video, where is the first frame and p the last, directly supervising instruction- based edits. For videos, the structure matches our reasoning- token design: the first frame corresponds to the last corresponds to p. and all intermediate frames serve as reasoning tokens. Input and reasoning frames are encoded into latents by the video VAE as standard video frames, while the target frame is separately encoded and repeated four times to match the VAE's temporal compression. This design makes reasoning tokens optional at inference- the VAE decoder can still recover the target frame independently-whitg providing strong supervision for coherent transitions when present. Together, this joint training strategy allows the model to learn semantic alignment from image pairs while additionally learn temporal consistency grounded in video data.
Video Data Curation. Training with reasoning tokens requires diverse examples of how scenes evolve over time. To this end, we curate a large-scale synthetic dataset of 1.4M videos generated with state-of-the-art video generative models. We place particular emphasis on disentangling scene dynamics from camera motion, since unintended viewpoint shifts between the first and last frames could be misinterpreted as edits during training.
Our corpus covers three complementary categories: (i) Static-camera, dynamic-object clips produced by text-to-video models (Wan, 2025; Cosmos, 2025), where we append the postfix "The camera remains stationary throughout the video." to prompts and filter unstable clips using ViPE (Huang et al., 2025); (ii) Egocentric driving scenes, a critical world-simulation scenario, generated with the HDMap--onditioneed model of Ren et al. (2025a), which fixes the camera while explicitly controlling vehicle motion via bounding boxes; and (iii) Dynamic-camery static-scene clips from GEN3C (Ren et al., 2025b), which allow precise camera trajectory control while keeping the scene content fixed. Finally, to provide corresponding instructions y, we employ a VLM to caption each video with an editing instruction, summarizing the transition from input output frame, detailed in Appendix D.
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3.3 INFERENCE WITH TEMPORAL REASONING
To perform image editing efficiently at infer- ence time, we introduce two-stage method which allows the model to benefit from video reasoning tokens without incurring the full computational cost of generating a complete video. Intuitively, the first few noisiest steps of a flow/diffusion trajectory determine the global structure of the outcome, and hence to- kens more frequently attend across frames in the sequence. Hence, we incorporate video reasoning tokens in these first denoising steps, and omit them in later denoising steps to ob- tain the best balance between quality and com- putational cost. Pseudocode is provided in Algo. and visualization shown in Fig. 3.
Algorithm 1 Sampling process of ChronoEdit. When Temporal Reasoning is enabled, N, steps are taken with video reasoning tokens 2full. Setting r = 0 or N, recovers standard sampling w/o Temporal Reasoning.
Given: Denoising model Fo, ODE solver CDESoLve 4, L,,,, (') + Ze', trajectory rea- soning length
sequence of N time steps T [bmoz,-.. .., ..,4min).
reasoning timestep Nr. Input: Editing instruction tokens y, input image token ~ NN 1) with shape (r + 1) C 2 + concat
4- 0, é [[o]
while n < N do
if Nr then
v + Fs (a(j,,n,tyy,
zilll + -DESooveeV, i, 2 I[n + 1])
In the first stage, we concatenate clean input tokens Ze. sampled reasoning tokens r and noisy sampled output tokens Zp into one tem- poral sequence. Similar to image-to-videe generation, the model performs denoising on the concatenated sequence without modifying the Ze tokens. Rather than denoising all the way to clean latents, N,, steps denoising performed, and the partially denoised last latents of the temporal sequence corresponding to Zp are carried forward. In the second stage, the partially denoised output latent is concatenated behind the clean input latent and fully denoised for the remaining N N, steps. As in training, the output latent corresponds to four repeated frames to match the video VAF's temporal compression. After decoding RGB, the four frames typically collapse to the same image, and we take the last frame as the final edited result.
else if N. then
N, then
//nal + concatt(,, zyall (-
f (i
Zfinal & ODESooveeey Ziinll T(n 1])
I < n + 1 T(u]
x t- Decode Output: x
3.4 FEW-STEP DISTILLATION FOR FAST INFERENCE
To further accelerate inference, we employed distillation techniques to reduce the number of steps required for inference. Specifically, we utilized DMD loss (Yin et al., 2024) train 8-step student model. The gradient of the distillation objective is given by
LDMD = -Ee (f (F
F dz
Stake (
d6
where Sreal and Stake denote the score estimation from the teacher model and a trainable fake score model, respectively; f(-) is the forward diffusion process (i.e.. noise injection). We omit the conditioning term for simplicity. Through this training process, our model can significantly improve the inference speed while maintaining prompt-following ability and image editing quality.
EXPERIMENTS
We evaluate ChronoEdit in two configurations, with 14B and 2B parameters, denoted as ChronoEdit- 14B and ChronoEdit-2B. We evaluate both models across multiple datasets and editing tasks, compare them with both open-source and proprietary baselines, and ablate the contribution of different design choices. We further evaluate the variant of ChronoEdit- 14B with temporal reasoning (ChronoEdit- 14B-Think), and the step distillation (ChronoEdit- 14B- Turboo.
Training Details. ChronoFdit- 14B is finetuned from the pretrain model of Wan2.1 1-121-1-1-11B-- 720P (Wan, 2025) and ChronoEdit-2B is built upon Cosmos-Predict2.5-228 (Cosmos, 2025). Both

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Model
Medel bize
AAdd
Adjuxt
Extract
Replace
Remove
Hochground
Styte
Hyheldd
Action
Overall
Magachnedh (Phang is al., 2025a
0:96
2.84
1.58
1.51
1.97
1.38
1:35
2.58
1.62
1.22
1.90
Inits (innola sl., 2023)
0:96
2.45
1.83
1.44
2.01
1.30
1.46
3.53
1.20
1.46
1.55
AnyBii al, 2005)
0:96
3.15
2.05
1.83
2.47
2.23
2.34
2.85
1.56
2.63
2:45
Ulinide (2haa- 2004)
811
3.84
2.81
3.13
2.90
1445
235
3.70
1.91
2.98
2.70
OlmoCom ef 201299
1.31
2.94
2.45
1:31
2.9
ICEát (Zhasg stall, 2025)
3.15
293
3,08
3.84
200
Step1X (ar etol. 2025)
305
(Deng 3025)
UngWheld-V1
3.47
2.00
4.21
326
OuniCen2 (Wi 2025k)
7B
3.74
3.57
4.81
344
HIJXX Kontect (Dex) abc 20053
1231
3.76
2.66
3.76
4.38
351
FLUX Kontest [Prx (Labeo 2009
N7A
6.25
4.15
2.55
4.5
3.57
4.76
4.53
4.61
400
GPT Imag: (Blggh] [0pmnA) 2028)
N/A
4.61
4.35
2.00
4.35
3.66
4.57
4.91
396
4.89
4.30
Quen Image (Nl et 2025a
208
4.35
4,36
3.43
4.66
414
4.35
481
381
4,00
4.27
ChooneRdie-18
2B
4.30
4.29
2,67
4.21
4.50
4.40
4.60
3.00
4.81
4.13
-ilt---d----- (teepe) Chrom-Edi-144
148
4.36
4.58
3.28
4.11
4.00
4.31
4.31
367
4.78
4.13
148
6.48
4,39
3.49
4.66
4.7
4.67
4.8
382
4.91
4.42
Table 1: Quantitative comparison results on ImgEdit (Ye et al., 2025). All metrics are evaluated by GPT-4.1. "Overall" is calculated by averaging all scores across tasks.
models are trained using a learning rate of 2e - and weight decay of le - 3. Since the pretrained sample timesteps t E [0, from a logit-normal distribution with shift value set to 5 (Esser et al., 2024b), thereby oversampling the large-timestep region. The model is pretrained image pairs, with the first and last frames of each video also included as additional image pairs. During training. we adopt 1:1 ratio between image pairs and videos, where the video data used to learn video reasoning tokens. We empirically use 6 intermediate latent frames as temporal reasoning tokens, corresponding to 24 frames in pixel space, which results in a total of T 8 timesteps. Training is performed with batch size of 128. In the final stage, the pretrained model is fine-tuned on a high-quality supervised fine-tuning (SFT) dataset of 50k images and 20k videos, sampled at a 5:1 ratio for 10k steps. For ChronoEdit-1443 4Br-our,, we apply distillation loss with learning rate of 2: for 1500 steps, setting the update ratio between the student and the fake score model (Yin et al., 2024) to 5 for stable training.
model already exhibits strong capability in generating fine-graines details,
1.4 million videos and 2.6 million
Benchmarks. We evaluate our method on two complementary benchmarks. First, for general- purpose image editing, we the SigEddt--Basc-Bll Suite (Ye et al., 2025) which consists of 734 test cases spanning nine common image-editing tasks: add, remove, alter, replace, style transfer, background change, motion change, hybrid edit, and action. The benchmark is constructed from manually collected Internet images to ensure semantic diversity, with the action category primarily emphasizing human pose modifications. Model performance each task is evaluated using 1PT-4.1, which measures adherence to instructions, quality of the edit, and detail preservation (Ye et al., 2025).
While prior benchmarks for image editing assess visual realism and instruction alignment, they provide limited evaluation of physical consistency. We therefore develop PBench-Edit, an image- editing benchmark derived from the original PBench dataset (Pbench, 2025), designed to assess editing in physically grounded contexts.
The original PBench evaluates world-mode progress domains such as autonomous driving, robotics, physics, and common -ense reasoning. PBench-Edit repurposes its curated videos and captions for targeted editing tasks by selecting representative frames from each domain and pairing them with manually verified editing instructions. Unlike IngEdit-Action, PBench- Eedt covers a broader spectrum of real-world intraattons-sur as cooking, driving, and robot manipulation-resullee in benchmark that is both diverse and physically grounded. It includes 271 images in total (133 human, 98 robot, and 40 driving). Evaluation is performed with GPT-4. I using the same criteria as ImgEdit (Ye et al., 2025): adherence to instructions, edit quality, and detail preservation. Additional visualizations are provided in Fig. S4.
4.1 QUANTITATIVE EVALUATION
General-Purpose Image Editing Results. Tab. reports results on the ImgEdit Basic-Edit Suite (Ye et al., 2025). To ensure fair comparison with prior works in terms of compute cost, we disable Temporal Reasoning and evaluate ChronoEdit-14p as a pure image-editing model. ChronoEdit- 1 achieves the highest overall score of 4.42, outperforming state-of-the- art baselines. Among open- source models, FLUX.1 Kontext [Dev] is the most comparable in scale (12B vs. 14B). ChronoEdit- 14B surpasses it by +0.90 overall, with especially large improvements +2.51), remove (4.57 vs. 2.94, +1.63), while performing on par in style transfer (4.83 vs. 4.38). These results indicate the strong capability of ChronoEdit for instruction-dis driven edits that require spatial
extract (4.66 vs. 2.15,
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Fidelity
Identity Preservation
Visual
Overall f
Step1X-Edit (Liu et al., 2025)
3.39
4.52
4,44
4.11
BAGEL (Deng et al., 2025)
3.83
4.60
4.53
4.32
OmniGen2 (Wu al., 2025b)
2.65
4.02
4.02
3,56
FLUX.1 Kontext [Dev] (Labs al., 2025)
2.88
4.29
4.32
3.83
Owen-Image (Wu ct al., 2025a)
3.76
4.54
4.48
4,26
ChronoEdit-14B
4.65
4.63
4.43
ChronoEdit-144 14B--t-kBBThikk (N, 10)
4.31
4.64
4.64
4.53
ChronoEdit-1-BB 1BB-Think (N, 20)
4.28
4.62
4.62
4,51
ChronoEdit- 1anddit-------- (N, (0))
4.63
4.52
ChromoEdit-2B- (N, = 100)
4.17
4.61
4,56
4.44
Table Quantitative comparison results on PBench-Edit. All metrics are evaluated by GPT-4.1. "Overall" calculated by averaging all scores across dimensions.
"Add Lifeguard wearing red uniform to the white lifeguard tower"
"Change the vehicle in the picture to be set in a beach environment"
"The toy cars be lifted off the table and held in each hand"
Reference Image
FLUX.1 1 [Dev]
OmniGen2
Qwen-Imen Image
ChronoEdit
Figure Comparison with baseline methods. The first two rows show examples from the Image Basic-Edi Suite (Ye et al., 2025) benchmark, and the last row is from PBench Edit, where ChronoEdit- Think is evaluated with 10 temporal reasoning steps. In both benchmarks, ChronoEdit achieves edits that more faithfully follow the given instructions while preserving
and fine details.
and structural reasoning. Compared to the 20B open-source model Qwen-Image (Wu et al., 2025a) which scores 4.27 overall, ChronoEdit- 14B matches or outperforms its performance across all tasks. achieves stronger results challenging categories such background change (4.67 vs. 4.38) and action/motion edits (4.41 vs. 4.27), suggesting that joint image-video pretraining provides strong advantages for modeling dynamic consistency and scene transformations.
Notably, ChronoEdit-I1
It is also worth noting that ChronoEdiit- 14B--urbb which runs 6x faster than ChronoEdit- 14B (5.0s vs. 30.4s per image, with speeds measured on Nvidia-H100 GPUs), achieves results only 0.3 points below ChronoEdit- 14B, yet still outperforms FLUX.1 Kontext [Dev] and FLUX.1 Kontext [Pro] by margins of 0.61 and 0.13, respectively.
Moreover, we also report ChronoFdit-2BB results, which are 7x smaller than ChronoFdit- but works on-par with ChronoEdit- SeA--eitt---B-TT Tur-b.
World Simulation Editing Results. We evaluate our method on the PBench-Edi benchmark, which emphasizes physically grounded editing scenarios. As shown in Tab. 2, ChronoEdit- 14B achieves the highest overall score (4.43), outperforming strong baselines such as BAGEL (4.32), Qwen-Image (4.26), and FLUX.1 Kontext [Dev] (3.83). Notably, ChronoEdit- 14B delivers clear improvements in Action Fidelity (4.01 3.76 for Qwen-Image and 2.88 for FLUX.1 Kontext [Dev]). while also maintaining competitive results in identity preservation (4.65) and visual & anatomical coherence (4.63). Among the three evaluation dimensions, action fidelity is particularly important as it directly
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"Add a group of kindorgaricn children.
"Open the doors of the SUV and add a person who is trying to get out.
"A robotic arm bands over a to person."
A rebotic arm moves the potato to the green clipboard
Figure 5: Qualitative results on Physical-AI world simulation related tasks. All results are generated by ChronoEdit- 14B-Think. Each group shows a reference image (left) and the corresponding output (right). ChronoEdit produces edits that accurately follow the given instructions while preserving scene structure and fine details in Physical Al-related scenes.
Figure 6: Temporal reasoning trajectory visualization. By retaining intermediate reasoning tokens throughout the entire denoising process, ChronoEdit- Bhhnhink able to visualize internal "thinking process" when performing edits. Sequences are shown from to right: the reference image (blue box), decoded intermediate reasoning frames (orange boxes), and the final target frame (green box). Top example prompt: "Add a cat on the bench". Bottom example prompt: "Place cake on plate by hand".
reflects a model's ability to maintain physical consistency when performing edits involving real- world interactions. Even without Temporal Reasoning, ChronoFdit-144 benefits from its pretrained video prior, enabling it to achieve stronger results than all baseline image-editing models.
With Temporal Reasoning, ChronoEdit- score of 4.53, with a particularly strong gain in Action Fidelity (4.31). This highlights the value of explicit Temporal Reasoning for edits that demand deeper understanding physical consistency. Notably, ChronoEdit-22 2B 2-thhink (N, 10) matches the performance of ChronoEdit- falling only slightly short of ChronoEdit- 4hiThink.
(N, 10) achieves
state-of- tate-offthhe-ar overall
4.2 QUALITATIVE EVALUATION
Comparison with Baselines. We compare our approach against state-of-the-art image editing methods across a variety of challenging scenarios. As illustrated in Fig. 4, ChronoEdit consistently produces high-quality results, demonstrating competitive overall performance and, in particular, clear advantage in action-oriented edits where precise modeling of dynamic poses and interactions required. These results highlight the effectiveness our video reasoning in handling complex, temporally grounded edits that often difficult for conventional editing approaches.
ChronoEdit on Physical AI Tasks Figure showcases ChronoEdit 's capability to address a broad spectrum of Physical-AI world simulation tasks. These results demonstrate the model's strong generalization across diverse domains of world simulation tasks, ranging from self-driving dynamics to robotic object manipulation.
Temporal Reasoning Trajectory Visualization. If the video reasoning tokens are fully denoised into a clean video, the model can illustrate how it "thinks" by visualizing intermediate frames

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Reference Image
ChronoEdit
ChronoEdit Currro Turbo
Reference Image
ChronoEdit
ChronoEdit-Tune
Figure 7: Qualitative result of ChronoEdit- Turbo. The lightweight ChronoEdit-Tun Turbo (runtime 5.0s) achieves editing quality similar ChronoEdit (runtime 35.3s) while offering improved efficiency. (Left: "Extract the red telephone booth in image". Right: "Replace the bicycle in image with wooden park bench".)
(a) Reference Image
(b) N, = (Runtime: 30.4s)
(c) N, = 10 (Runtime: 35.3s)
(d) N, 20 (Runtime: 40.2s)
(c) N, 50 (Runtime: 55.5s)
Figure 8: Qualitative ablation on video reason step N,. Empirically, found that setting the reasoning timestep to N, 10 within total of N 50 sampling steps achieves performance that comparable using reasoning across the full trajectory. Example Prompt: "Halve the poached egg to reveal the yolk", Reported runtime is measured on Nvidia-H100 GPUs.
reasoning trajectory-though at the expense of slower inference. We present such a visualization in Fig. 6. As illustrated in the top row, when prompted to "add cat on the bench", the model first synthesizes the bench and then anticipates the cat emerging from the corner and leaping onto composing the scene through a sequence of plausible intermediate states. Notably, an emergent capability of our approach is ability to generate reasoning trajectory videos realize edits. Even without exposure to training data where, for instance, bench suddenly appears, the video model can still imagine and execute plausible trajectory to accomplish the edit. In another example, the model correctly infers the stepwise process of placing cake on a plate by hand. This deliberative trajectory reveals how the model perceives and interacts with the world in a coherent, physically grounded manner (see video visualization in Project Page).
We further visualize the qualitative comparison of ChronoEdit and ChronoEdit- 14B-Turbo in Fig. 7. Both ChronoEdit and ChronoEdit- Turbo successfully execute the edits with comparable visual fidelity, preserving scene structure and fine details. This demonstrates that the lightweight ChronoEdit-Tuun Turbo variant achieves editing quality comparable to that of ChronoEdit, while offering improved efficiency (5.0s vs. 30.4s runtime).
ChronoEdit-Turee
4.3 ABLATION STUDY
Reasoning Timestep. discussed in Sec. 3.2, our model performs reasoning by traversing sequence of intermediate states, thereby constructing a plausible temporal trajectory instead of directly regenerating the target image a single step. Empirically, we found that setting the reasoning timestep to N, 10 within a total of N 50 sampling steps achieves performance that is comparable to using reasoning across the full trajectory (Tab. 2), while reducing the overall computational overhead from 55.5s (N, 50) to 35.3s (N, 10), which is a small 4.9s increase compared to not using temporal reasoning (30.4s). An illustrative example is provided in Fig. 8, highlighting that shorter reasoning horizons often sufficient maintain fidelity while offering substantial efficiency gains.
Ablation studies on the benefits video pretrained weights and encoding editing pairs design can be found in Appendix C.
CONCLUSION
We introduced ChronoEdit, a foundation model for image editing designed to enforce physical consistency. By repurposing a pretrained video diffusion model and introducing a temporal reasoning inference stage, approach preserves coherence between input and edited outputs while producing plausible transformations. Extensive experiments demonstrate that ChronoEdit achieves state-of-the the- art performance among open-source models.
10

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6
ACKNOWLEDGEMENT
The authors would like to thank Product Managers Aditya Mahajan and Matt Cragun for their valuable guidance and support. We further acknowledge the Cosmos Team at NVIDIA, especially Qinsheng Zhang and Hanzi Mao, for their consulting of Cosmos-Pred2.5-25 We also thank Yuyang Zhao, Junsong Chen, and Jincheng Yu for their insightful discussions. Finally, we grateful to Ben Cashman, Yuting Yang, and Amanda Moran for their infrastructure support, especially in the period leading up to the deadline of this work.
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Reviews:
[{"review_id": "Om8mVWde76", "reviewer": "Reviewer_dEVi", "summary": "This manuscript proposes a ChronoEdit method for image editing, which reframes the editing problem as a video generating problem. It introduces a temporal reasoning mechanism at inference to perform editing, wherein reasoning tokens are designed to imagine plausible editing trajectory. A novel benchmark dataset is proposed to evaluate the proposed method.", "questions": "See the weakness", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "-\tThe idea of solving image editing using video generation models is interesting.\n-\tExperimental results on the benchmark datasets show good performance.", "weaknesses": "-\tThe proposed method is prone to the video generation models. Is the upper bound of the proposed method is limited to the performance of the video generation models?\n-\tThe computational complexity of the proposed method vs. other image editing methods is expected to be justified.\n-\tThe ambiguity of the “edited” image. As mentioned by the author in L216-232, the author formulates the image editing as a T-frame video generation problem, wherein 0- and T-th frames are defined as the input and edited image. Can the (T-1)-th frame or other frames also be considered as the “edited” image? If not, what are the key differences between the (T-1) and T frames?\n-\tThere are methods [1-3] that apply chain-of-thought techniques to the image editing problem, which also attempt to add an intermediate process into the editing problem. Can you discuss and justify the proposed paradigm vs these methods? \n\n[1]. Enhancing Image Editing with Chain-of-Thought Reasoning and Multimodal Large Language Models\n[2]. ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hOKA8t7RU0", "reviewer": "Reviewer_BTgq", "summary": "The paper introduces ChronoEdit, a novel framework designed to instill physical consistency in generative image editing, a capability deemed crucial for \"world simulation\" applications (e.g., robotics, autonomous driving). ChronoEdit achieves this by reframing image editing as a two-frame video generation problem, thereby leveraging the learned temporal prior from pre-trained video generative models. For enhanced coherence, the framework features a Temporal Reasoning Stage (ChronoEdit-Think) during inference. Here, \"reasoning tokens\" (imagined intermediate video frames) are jointly processed with the input to implicitly constrain the final edit trajectory to one that is physically plausible. The authors validate their approach with the new PBench-Edit benchmark, demonstrating SoTA performance in both visual quality and physical realism over existing methods.", "questions": "1. What editing operations fundamentally cannot benefit from this framework?\n2. Is rejection sampling effective for generating physically plausible results?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of repurposing the powerful temporal mechanisms of video generation models to solve a fundamental deficiency (physical inconsistency) in static image editing is a good idea, providing a principled method for incorporating dynamic laws.\n2. By focusing on physical consistency, ChronoEdit addresses an important bottleneck for real-world applications like autonomous systems, where geometric or physical inconsistencies are unacceptable.\n3. The Temporal Reasoning Stage is cleverly implemented to optimize efficiency by limiting the reasoning steps ($N_r$) and discarding the tokens early in the denoising process. Furthermore, the visualized reasoning trajectory offers a degree of interpretability into the model's \"thinking process.\"\n4. The creation of the PBench-Edit benchmark is a valuable contribution, establishing a much-needed standard to evaluate models on physical and temporal coherence, pushing research beyond purely aesthetic metrics.\n5. Figure 4 looks great", "weaknesses": "1. Despite efforts to optimize, the ChronoEdit-Think variant still introduces a measurable inference overhead compared to non-reasoning baselines.\n2. The framework’s success depends entirely on the video model's ability to perfectly encode and enforce physical laws within its high-dimensional latent space. If the latent representation merely captures statistical correlations of motion rather than strict physical principles, errors in complex or novel dynamics will inevitably persist.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ui4whNQWBr", "reviewer": "Reviewer_rm73", "summary": "The authors propose a method termed ChronoEdit using latent diffusion/flow video models for editing images based on text prompts and a reference image. Three variants of the method are proposed in the paper. (1) ChronoEdit - generations of the edited image as a video having two frames (the reference image and the output image); (2) ChronoEdit-Think - where additional \"reasoning\" frames/tokens are added between these two frames in order to model better physical consistency across time; (3) ChronoEdit-Turbo - distilled version of ChronoEdit for few-step sampling. The paper also presents PBench-Edit, a new benchmark for image editing designed to assess editing in physically grounded contexts. The authors compare the 3 variants to various baselines from the literature on two benchmarks and show an ablation study for various aspects of their method.", "questions": "* In line 201, how is it ensured that F', c, and w are integers?\n* Was the model from which you generated the training dataset (\"video data curation\") trained (either in the pre-training stage or fine-tuning) on PBanch?\n* All baseline methods seem recent; why is there such a gap in the number of parameters? Specifically, I assume that at least part of the baseline methods also use latent diffusion/flow models. What causes the difference then? And can you evaluate your method using exactly the same network and number of parameters?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The main novelty of this paper, as I understand it, since I am not familiar with the literature in this area, resides in the idea to frame the problem of image editing based on text prompts as a video generation task using diffusion/flow models. By doing so, one can get better physical consistency. Indeed, I find this idea novel and interesting.\n* To gain better control and interpretability, another novel part of the method is to introduce the so-called reasoning tokens, which show that the authors took an extra step in the modeling process of the problem.\n* The authors introduce a new benchmark, which I suppose can be valuable for research in this area.\n* The method seems to improve over baseline methods on both benchmarks, both quantitatively and qualitatively. \n* For the most part, the paper is written clearly.", "weaknesses": "* While I do appreciate the idea of using video generation models for image editing and the other improvements to the method, in terms of the overall contribution and novelty, to me, this work seems limited. To properly assess its contribution, I believe it will be valuable if the authors could further elaborate on how this work lays a new foundation for future models in this area and if it opens new avenues of research.\n* Regarding the experiments:\n  - To me, the quantitative results are not clear enough. Specifically, are the numbers in the table bounded between [0,5]? If not, are they bound in another region? How exactly are they measured? In addition, in order to understand the significance of the results, std information should be reported.\n  -  The authors present only successful cases for their model, but what are some failure cases of it? Are there common situations in which it systematically fails?\n  - One experiment I found missing is showing the performance of the method when using out-of-the-box video generation models (without training), where the first and last frames are set as in this paper.\n  - Regarding reproducibility, code wasn't provided. For me, this is a weakness in an empirical paper.\n* Minor:\n  - In Fig. 3, what is the prompt?\n  - In line 311, do you mean $N=8$?\n  - It is not clear which version of ChronoEdit, ChronoEdit-Turbo distills.\n\nFor now, my tendency is to reject the paper; however, as I am not an expert in this field, I would like to see the author's response, assessment of my fellow reviewers, and the AC before making a final decision.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mHDM9FKd1e", "reviewer": "Reviewer_kjZH", "summary": "This paper proposes modeling the physical realism of image editing using video generation models. By leveraging the strong temporal, physical, and motion consistency capabilities of video generation models, the approach achieves impressive editing results. Furthermore, the introduction of a temporal reasoning token to simulate intermediate video frames is a very intuitive idea, and the experimental results are remarkable.", "questions": "See the weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Utilizing video generation model to model the image editing task, which introducing great physical prior, achieving great results.\n2. The Temporal Reasoning Token simulate the intermidiate step of video and it fills the gap of modeling intermediate changes in image editing, resulting in stronger interpretability.\n3. After distilling, the result is still great and the speed is nearly comparable with image editing model.", "weaknesses": "I do not have many concerns regarding the content of the paper itself. However, I am curious about how the video generation based model would perform in multi-turn editing scenarios involving different user instructions, where each round would introduce an additional input frame, which is an interesting attempt for future development.\nAdditionally, I want to know the true performance of trained video generative model in video generation. This would provide insights into how demanding the requirements are for training video generation models to achieve high-quality image editing. Please show the result in video generation benchmark like VBench series.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)