FreeViS: Training-free Video Stylization with Inconsistent References
Authors: Jiacong Xu
Yiqun Mei
Ke Zhang
Vishal M. Patel
Venue: ICLR 2026 Poster (2026)
Keywords: Style Transfer
Video Editing
Video Diffusion
Primary area: generative models
TL;DR: High-quality video stylization without training

Abstract: Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our method integrates multiple stylized references to a pretrained image-to-video (I2V) model, effectively mitigating the propagation errors observed in prior works, without introducing flickers and stutters. In addition, it leverages high-frequency compensation to constrain the content layout and motion, together with flow-based motion cues to preserve style textures in low-saliency regions. Through extensive evaluations, FreeViS delivers higher stylization fidelity and superior temporal consistency, outperforming recent baselines and achieving strong human preference. Our training-free pipeline offers a practical and economic solution for high-quality, temporally coherent video stylization.

Published as a conference paper at ICLR 2026
FREEVIS: TRAINING-FREE VIDEO STYLIZATION WITH INCONSISTENT REFERENCES
Jiacong Xu Yiqun Mei Ke Zhang' Vishal M. Patel
1 Johns Hopkins University 2 Adobe Research
fixul55, k vpate136) jhu edu ymei@adobe.com con
/ttps://xujiae geth github. wtw.euuu..oFFFeeeeeeeeeee io/Freevis/
Style
Video Stylization
FreeVis
Content
Anyv2v
Propagation Error
Style
Style
Stylized Video Generation
StyleMaster
StyleCrafter
FreeVis+Wan
FreeViS+Wan
clenteboarder doing Evicks people watch.
VaCIcumiing the livinng floor.
Figure 1: Previous works (e.g., VnV Ku al. (2024)) suffer from propagation errors inherent to their single-reference inputs. Combined with a text-to-video model Wan et al. (022)), FreeViS outperforms existing methods Ye al. 2025 Liu et al. (2024a) on stylized video generation.
ABSTRACT
Video stylization plays key role in content creation, but it remains a challeng- ing problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training dedicated video stylization model typically requires paired video data and is computationally ex- pensive. In this paper, propose FreeViS. ViS, training-free -freeng--feeeee video stylization frame- work that generates stylized videos with rich style details and strong temporal co- herence. Our method integrates multiple stylized references to pretrained image- to-video (12V) model, effectively mitigating the propagation errors observed prior works, without introducing flickers and stutters. In addition, it leverages high-frequency compensation to constrain the content layout and motion, together with flow-based motion cues preserve style textures in low-saliency regions. Through extensive evaluations, Free ViS delivers higher stylization fidelity and su- perior temporal consistency, outperforming recent baselines and achieving strong human preference. Our training-fres pipeline offers practical and economic so- lution for high-quality, temporally coherent video stylization.
INTRODUCTION
Style transfer has been a Long-standing research topic in computer vision, with applications span- ning art, education, advertising, and entertainment. In recent years, tremendous progress has been made in image style transfer Gatys et al. (uang) & Belongie (2017); [iu et al. (202T); Chung Liu
et al. b Xu al. (2025b) driven by advances in deep learning architectures and generative

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modeling. While the focus has been still images, videos have become more popular medium for everyday communication in recent years. Yet video stylization remains significantly underexplored compared to its image-based counterpart. As content distribution changes across time, naively ap- plying image stylization frame by frame leads to severe flickering artifacts, resulting in poor tem- poral consistency. Even though advanced temporal smoothing techniques Duan et al. (2023) can mitigate flickering artifacts, they often do so at the expense of losing style richness, resulting in overly smoothed textures and reduced visual plausibility.
Previous video stylization works Wang et al. 20200;; Pande & Sharma 2023) have attempted to improve temporal consistency by modifying convolutional architectures. However, their quality re- mains inferior to that of recent ceffusiion-based image stylization approaches. More recently, several diffusion-basee methods Liu et al. (2024a): Ye et al. (2025) have been proposed for stylized text-to- video (T2V) generation, but these approaches are not directly applicable to video-to-video (V2V) editing. In addition, our experiments also found that neither recent unified video editing frame- works Jiang et al. (2025) nor text-driven editing methods Geyer et al. (2024) are able to perfectly transfer style across videos. Beyond these works, directly fine-tuning a video diffusion model (e.g., DiT-based architectures) is also impractical, as it demands large-scale which are difficult to obtain. Simply adopting the perceptual training strategy, as in image styl- ization Liu et al. (2021) cannot ensure temporal consistency across frames. Moreover, substantial computational resources are required for fully finetuning.
iigginal-stylize video pairs,
On the other hand, reference- baseed video editing methods, such Any Ku al. 2024), can leverage image stylization models to stylize the first frame, and then propagate it to the rest of the frames using pretrained image-to-video (I2V) models. In this way, no training or only limited training Ouyang et al.] (2024) is required. However, due to the absence of stylized videos in the training phase of the base 12V model. it fails to properly handle the reference frame as it is out of the training distribution (see Appendix (..7), thus incapable of parsing and propagating style patterns from the first edited frame. As Figure shows, these reference- based methods struggle to transfer style patterns to novel content in subsequent frames, resulting in pronounced propagation error.
In this paper, we aim to tackle video stylization in a training free manner, i.e. by inverting diffusion model, to achieve comprehensive style transfer while preserving temporal coherence. An intuitive solution to address propagation error is to provide the base model with multiple reference frames across the entire video. However, naively concatenating additional reference frames with noise latent leads to severe flickering and stuttering artifacts in the results. To address this, we introduce isolated attention with carefully designed masking strategy mitigate stylization inconsistencies, together with a novel approach to inject temporal dynamics into the reference latents. We also found that the inverted noises alone are insufficient to accurately reconstruct original video content, often missing structures in stylized results. To this end, we propose strategy to extract high- frequency components from the reconstruction compensation of PnP inversion Ju et al. (2024) to compensate and constrain spatial layouts and motion trajectories. Lastly, in plain regions with few salient features, style textures may vanish under large camera motion. To overcome this, we propose method that leverages optical flow to constrain diffused attention areas in associated frames.
The contributions of this paper are summarized as follows: (1) We propose a novel reference- and maining-free framework: Fiee for video stylization, which effectively addresses the propa- gation error inherent in existing methods; (2) We conduct extensive experiments demonstrating the effectiveness of our design and showing that Free ViS achieves higher stylization quality compared to prior approaches; (3) We show that, when combined with existing T2V models, FreeViS also achieves stylized T2V generation with performance comparable to recent training- -based methods.
2
OBSERVATIONS
2.1
FREQUENCY ANALYSIS OF INTERMEDIATE LATENTS
Since the inverted noises alone cannot accurately reconstruct the videos (Appendix A.7), we adopt PnP Inversion Ju et al. (2024) to progressively align the denoising trajectory with the inversion path. Building on this framework, we investigate the influence of low- www-freuuncy (LF) and eighffrequency (HF) components of intermediate latents along the denoising process (Appendix A.8). Recent image editing studies Yang et al. (2023): Xu et al. (2025b) have shown that the HF components of the initial noise primarily determine the spatial layout of the generated images. Here, we observe similar
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phenomenon in 12V models: LF latents primarity govern the appearance and color distribution of the generated video, whereas HF components encode layout and motion cues.
2.2 CROSS-FRAME ATTENTION
Recent advances in video generation Kong et al. (2024) Wan al. (2025) have seen a shift in architectural design from UNet-based models Blattmann et al. (0033) to Transformer-based DiT frameworks PPeelles & Xie (023). For finer-gran iner-grained analysis of attention patterns. we decompose the cross-frame attention in the I2V Wan model into temporal and spatial components.. To visualize temporal attention, we spatially average the attention values across each frame. For spatial attention, select a point on the query frame and record the distribution of attention intensities across other frames. As shown in Figure 2 the model exhibits an auto-causal attention pattern, with lower attention values in the upper triangular region of the temporal attention matrices. Notably, the second frame (immediately following the reference frame) consistently receives high attention throughout the entire denoising process. This suggests that the reference frame influence the generation of all subsequent frames indirectly, by strongly guiding the denoising of the second frame.
During late stages of denoising, the model concentrates on refining local details, such as textures, resulting in more accurate pixel-level correspon- dences compared earlier stages that primarily require global information for layout establishment. This re- flected in the sharper and more fo- cused high-attention regions. How- ever, when attending to frames that are temporally distant from the query frame, the pixel-level attention tends to diffuse into incorrect areas, likely due increased motion (camera objects) between distant frames rela- tive to adjacent ones. Note that accurate pixel correspondences crucial for preserving the consis- tency of local stylized textures or strokes in reference- video stylization. Therefore, an external control strategy required to regularize the attention area for better appearance preservation.
Step
Stup
Stop
Supp 20
Suep 40
Step 45
Step 5
Stap
Figure Visualization of cross- fras- temporal (upper) and spatial (lower) attentions in different timesteps.
METHOD
Our framework builds upon pretrained 12V diffusion model, which serves the backbone. Given arbitrary style image, stylized references are generated via an image style transfer model applied to several selected content video frames. We leverage inversion to recover the denoising trajectory and initial noise. The pipeline comprises two branches: reconstruction and stylization. Each branch is conditioned the corresponding selected references. The reconstruction branch provides query, key, and value matrices to the stylization branch in every DiT' block. Parameters specific to the reconstruction and stylization branches are denoted by superscripts r and s, respectively. The subseript R denotes parameters associated with additional references beyond the first frame. After each denoising step, the tigh-frequency components of the difference between the target latent x; and reconstruction latent x, are added to the stylization latent x/. The value matrices v² projected from additional stylized references are injected with dynamic clues, as formalized in Equation
The overall architecture the proposed FreeViS pipeline illustrated Figure 3
3.1 INDIRECT HIGH-FRRQUENCY COMPENSATION
We adopt PnP Inversion Ju et al. (2024) to provide additional guidance throughout the denoising process. Specifically, the full denoising trajectory [x,, Xt- Xo] is cached during inversion and treated as the target latents. In the original PnP framework, the difference between the target latent X, E x where is the number of encoded latent maps, and the current reconstruction latent x, Raxexhxx is computed and applied to both the reconstruction and editing branches, enabling near-exact reconstruction. However, observe that this strong correction directly drives
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Refeencer
Inversion
Inversion
Dir Block X
Video Lelent
Cross Attt
Isllated Artn
Crass Attn
bolated
FFN
Encode
Stytted Abferences
[o%.of]]
[e".agl
HPF
(ATA
1R7.RA
HPF
Block
Misolated Atn
Stylization
Mtroloted
Cross-tti
MFN
Crats Atn
Encoue
Sty byde Canaet
Video
Rel Mels
High- Pars-Pass litee
45
Atto
M-Sitt
Elerrent-wsse
Summittion
RE
91
Masled Self
Warping
96
16
10
Fum Mob
Sobtaction
AEDI
Oprical
Sequeore Contalanation
26
A6
Figure 3: Overview of the ViS pipeline. Isolated-Attn indicates the ① mode, while MIsolated-
Attn includes @ and @ attention modes. Optical flow, extracted using RAFT Teed & Deng (2020). generates reference and flow masks for masked attention in attention modes @ and 3
all the information of the stylized latent toward the original target, resulting in undesirable color distribution shifts from the stylized video back toward the original content.
As analyzed in Section 2.1 low-frequency (LF) latents primarily control appearance and color, while high-frequency (HF) components encode layout and motion. This motivates Indirect High- frequency Compensation (IHC) strategy, which injects only HF differences into the stylized latents, preserving the stylized appearance while correcting structural inconsistencies. In the reconstruction branch, full compensation is applied to recover video content: x = A (x, x,) + x", where is hyperparameter linearly decaying over timesteps. To further align the color distribution, we firstly apply AdaIN Huang & Belongic (2017) on both and x before calculating their difference:
= o Xx (x(,) (xx)
x [4(x;)
+ (xx)),
T(x)) = ox²))
((xf),
(1)
(x²)
where x; refers to the stylization latents in current timestep f, and (a-) and (()) denote channel- wise mean and standard deviation, respectively. then compute the Fast Fourier Transform (FFT) of the reconstruction difference along spatial dimensions to obtain the frequency representation. A low-pass filter HLP is applied to isolate HF components, which are subsequently transformed back via inverse FFT (iFFT) and added to the stylized latent:
x² = (F (T(x,) T(x))) 1-1-Ho HLP) +x/,
(2)
where F(-) and F-)(-) denote FFT and iFFT. Empirically, the cutoff frequency of the low-pass filter be 0.2 for the best trade-off between style fidelity and content reconstruction. The experi- mental results show that IHC successfully preserves the stylized color and texture while enhancing spatial consistency and motion fidelity, particularly in scenes with significant camera movement or when later frames differ substantially from the first frame.
3.2
ADDITIONAL INCONSISTENT REFERENCES
Previous 12V-based video editing methods Ku et al. 2024) Ouyang et al. (2024) typically rely on single stylized first frame to guide the stylization of the entire video. While effective when content changes are minimal, this approach struggles to propagate style features to regions that differ significantly from the first frame. An intuitive solution to incorporate multiple stylized references. However, existing advanced I2V models support only single reference input. Naively concatenating additional reference tokens with reconstruction or stylization noise tokens prior to self-attention often results in significant flickering and stuttering in the generated videos.
To enable multi-reference inputs in the reconstruction branch, we propose the Isolated-Athe strat- egy, which isolates the influence of auxiliary references xf, thereby preventing interference with
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the original denoising schedule. Specifically, the reconstruction tokens x' undergo standard self- attention, while the reference tokens X attend to both reconstruction and reference keys and val- ues. This design allows reference tokens to evolve in syne with the denoising process, mimicking full self-attention behavior. Denote the Query, Key, and Value matrices projected from x" and xtt as (Q", K", Vr) and (Qr: Ki Vid, respectively. Then, this mechanism can be defined as:
Out" - A(Q", K", V*) e A(Q K" @ K V Vi))
(3)
where A(-) refers attention operation, and @ denotes sequence- token concatenation.
Differently, in the stylization branch, full information exchange among all tokens is essential for comprehensive style transfer. However, since the additional stylized references x are indepen- dently encoded, the value matrices V projected from X lack dynamic information, which causes the stuttering issue in adjacent frames. We observe that appearance information can be isolated and exchanged between videos with shared dynamics (see details in Appendix AA.5p. Since the dynamic information shared between stylization values V and reconstruction values V", we decouple the dynamic residual by computing their differences relative to their respective references (only contain appearance), and inject only the dynamic component into Vh. This is implemented as:
(4) where in denotes the positional indices of the references, and & increases linearly from 0 to 1 over the timesteps. Early in the denoising process, the model relies more dynamics from the recon- struction branch; toward the end, V converges to [iii to ensure consistent final outputs.
V Vi + (V*[ia) Vin + - E) (V'lia) - Vir)
Since inconsistencies naturally exist across stylized references, the same region may exhibit varying appearances, resulting in time-variant stylization artifacts, such as flickering. To address this issue, we leverage optical flow extracted from the content video identify regions already covered by carlier references and mask these regions in attention, thereby resolving appearance conflicts. We employ RAFT [Ted Deng (2020) compute optical flow and trace pixel correspondences from the first reference frame to subsequent reference frames. The reference mask MRef is constructed such that position is marked as False if the corresponding pixel is reachable from previous references(see Appendix A.3). Masked attention (Mrasnnd is implemented to incorporate MRaf:
(5) where {Q", K*} and (Q K2 refer to the Query and Key matrices projected by x" and x Inspired by prior UNet-based approaches that forward convolution and attention features from re- construction to editing branches, leverage QK OK-Sharing of the reconstruction queries and keys to enable more flexible attention operations in the stylization branch. These queries and keys de- fine spatial-temporal correspondences across the content video, which critical for effective style propagation and temporal consistency. Thus, we replace the query and value in Equation 5
Out = A Q" K" E K V ED V MReJ) @ A(Q K* K. @ Vi),
Out AMasked Q", K" KT. V @ Vi. MRes) e A(Q K" + K V
Vip (6)
Due to the auto-causal nature of the base model, the style of the generated video is primarily deter- mined by the first reference frame, while subsequent references act as supplementary sources.
3.3 EXPLICIT OPTICAL FLOW GUIDANCE
When the camera or object motion is significant, we observe disappearing or time-varying style textures in plain areas. We attribute this issue to inaccurate attention maps between temporally distant frames, especially in regions with little salient visual features, as discussed in Section 2.2 To address this, introduce Explicit Optical-flow Guidance (EOG), which provides near pevel-level correspondence via optical flow. We first compute forward and backward optical flows and trace every pixel across frames. If pixel př, in frame maps location p'm,, in frame the flow mask MFlow E {0, 1)T x xxxxxxxxxxxx xxxm is set to True at index (s, i, t, m, n) (see Appendix A.4 We further apply dilation to compensate for estimation errors of the optical flow.
Using this mask, we perform masked attention on stylization and corresponding reference tokens:
Out AMasked Q + Q K K V V Mylow A Maef),
where A denotes a logical AND, ensuring that only consistent regions across flow and reference masks contribute. This design constrains the attention area of the source pixel on every frame,
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Stylization Quality
Video Consistency
Method
HP
CSD Score
ArtFID
FID i
LPIPS 1
S
MS
FC L
Reference
0.508
31.62
20.28
0.486
0.918
0.986
0.000
TokenFlow
0.111
37.87
27.94
0.309
0.915
0,976
1.092
2.179
VACE
0.138
35.53
27.77
0.240
0.910
0,984
0.554
2.895
12VEdit
0.331
38.72
22.53
0.653
0.738
0.975
2.074
2.538
Any
0.267
35.84
23.52
0,471
0.753
0.961
1.715
2.443
AnyV2V*
0.270
34.81
27.59
0.218
0.675
0,983
1.103
3.372
Ours
0.448
33.46
21.62
0.479
0.898
0.978
0.641
4.113
Table 1: Quantitative results for video stylization. "SC", "MS", "FC", and "HP" denote Style Consistency, Motion Smoothness, Flow Consistency, and Human Preference, respectively. We use the stylized references and original video anchor the quality of stylization and video consistency.
explicitly controlling the appearance to follow the video dynamics. Empirically, we find that using queries and keys from stylization tokens outperforms those from the reconstruction branch. Finally, the outputs from all three attention modes are aggregated before cross-attention as:
Out (1 -1- -) Outi + 8 Out + 7 Out
7)
where 3 and y are two hyperparameters, controlling the impact of the inconsistent references and the strength of the explicit appearance guidance. 7 is set to a non-zero value only in the final stage of the denoising process, when the model primarily focuses local texture refinement.
The base model further encodes the reference image using CLIP Radford et al. 2021 to provide high-level semantic guidance via cross-attention To incorporate multiple refer- ences, we concatenate the CLIP features from all reference frames and apply QK-Sharing in the cross-attention layer, enhancing the injection of language-aligne' information. As CLIP features contain limited spatial structure, reference and flow masks are not applied.
Cross-Attention.
Reference Arrangement. In principle, all the frames can be stylized and used as references in Free for comprehensive style coverage. Nevertheless, increasing the number of references sig- nificantly raises computational and memory costs. Considering that most advanced I2V models are limited to short videos (typically around 81 frames), we select the first, middle, and last frames as references, an empirically effective choice for achieving high-quality stylization in most cases. Fach reference token is assigned the same positional embedding as its corresponding frame latent to guarantee correct spatial and temporal propagation of stylized features.
EXPERIMENTS
This section divided into two major parts: Video Style Transfer and Stylized T2V Generation. All the stylized references for Free and other reference-based video editing models are provided by InstantStyle-plus Wang et al. (2024b). We deliver the results of FreeViS combined with various image stylization methods and different video diffusion base models in Appendix A.10 and A.18
4.1 VIDEO STYLE TRANSFER
Dataset & Metrics. We curated 200 online videos covering human activities, natural and urban scenes, animals, and transportation. Style images were sourced from WikiArt Tan al. (2018) and web collections. For evaluation, we use the CSD Score Somepalli et al. (2024) to assess frame- style similarity, and ArtFID Wright & Ommer (2022) to jointly evaluate stylization quality and content preservation via FID and LPIPS. Motion smoothness is measured with VBench Huang et al. (2024),
and optical flow consistency between the original and stylized videos with End-Point Error (EPE) Barron et al. (1994). Finally, we introduce Style Consistency, which quantifies the similarity be- tween the first frame and subsequent frames based on style features extracted by Somepalli et al. (024).. We conduct human preference evaluation to assess both the visual plausibility and the stylization quality of the generated videos. Each examiner is asked to assign a score from to 5 for both aspects, and the final rating is obtained by averaging the two scores.
Baselines. We compare against various baselines: reference-based video editing (Any V2V Ku et 2024), 12VEdit Ouyang et al. (2024)). text-driven editing (TokenFlow Geyer et al. (2024) unified

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Style
Content
Style
Content
WU DOY HOS
FreeVis
FreeVis
Anvv2V
AnyV2V
TokenFlow
TokenFlow
VACE
Figure 4: Qualitative comparison of FreeViS with other video editing methods on video stylization. The areas inside the bounding boxes show missing style textures and incorrect reconstruction.
Style
Content
Style
Content
FreeViS
FreeViS
InstantStyle-Plus
AdaAttn
ReReVST
ReReVST
Figure 5: Qualitative comparison Fiee with previous video and image stylization methods.
The flickering issue of the image stylization methods be observed in the supplemented video.
video editing (VACR Tiang et al. (0255) ) classic video style transfer (ReReVST Wang et al. (2020)
ViS13D Pande & Sharma (2023) ), and image style transfer with temporal smoothing Duan et al. (2023) (AdaAttn Liu al. (2021), InstantStyle-plus WWang et al. (2024b)). Since the code for V2V implementation of StyleMaster Ye et al. (2025) has not been released, we omit its results currently. We re-implement V2V, named by V2V+ using the same base model as FieeViS for fair comparison. Note that among all the models, only V2V and TokenFlow are training-free
Results. Qualitative comparisons are presented in Figures 4 and 5 VACE demonstrates strong video consistency (Table T but is limited to color modification, struggling to capture and transfer complex style textures. Similarly, TokenFlow faces challenges in style representation, primarily due to the limitations of text-based descriptions, which parallel the issues observed with VACE. Since the modifications from VACE and TokenFlow on the original videos mainly focus on color changes instead of new texture generation, the video consistency scores are naturally better than other methods and close to the reference anchor. Also, the MS and FC are computed using flow- estimation models pre-trained on natural videos. However, the stylized videos can be considered out-of-diitribution samples, leading to inaccurate flow estimates and degraded scores. The origi- nal Any V2V and 12VEdit models fail reconstruct the video when there are substantial content

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A child riding bioycle with training wheels.
parrots greeting tach other with beaks.
Style
Style
StyleMaster
StyleMaster
StyleCrafter
StyleCrafter
FreeViS+
FreeViS+Wan
A retriever running through a grassy field.
A squirel elimbbing up tres trank.
Style
Style
StyleMaster
StyleMaster
StyleCrafter
StyleCrafter
WreeViS+Wan
FreeViS+Wan
Figure 6: Qualitative comparison of Free with previous methods for stylized video generation. The difference can be easily observed in the supplemented video.
Stylization Quality
Video Generation Quality
Method
HP
CSD Score t
FID +
CLIP-Text
DQ t
MS 1
BC f
IQ
StyleCrafter 0.515
22.62
0.211
0.368
0.965
0.951
0.578
2.83
StyleMaster 0.221
26.04
0.243
0.123
0.985
0.945
0.667
2.55
Ours+Wan 0.437
24.63
0.264
0.509
0.980
0.941
0.691
3.97
Quantitative results for stylized video generation. "DQ". "MS", "BC", "IQ", and "HP" denote Dynamic Quality, Motion Smoothness, Background (CLIP) Consistency, Imaging Quality, and Human Preference, respectively. smaller DQ can naturally lead better MS and BC.
Table 2:
changes between the first and last frames (see Appendix A.17 likely due the weak priors of their pre-trained base models. While V2y significantly mitigates this issue, it still suffers from propagation errors: early-frame styles are not consistently transferred to later frames, resulting in the lowest style consistency score. In contrast, FreeViS effectively preserves stylistic features across the entire sequence, achieving the highest stylization quality and near-optimal temporal consistency.
Classical video stylization methods, which typically rely on non-diffusion architectures, offer strong temporal consistency but suffer from limited stylization quality. In contrast, image-based stylization methods, while capable of richer stylization, exhibit significant frame-wise inconsistencies. This leads to over-flattened style textures and persistent flickering artifacts, even after temporal smooth- ing. ViS overcomes these limitations by leveraging pretrained video diffusion framework that effectively balances high-quality stylization with robust temporal coherence.
4.2 STYLIZED T2V GENERATION
Dataset & Metries & Baselines. We utilize Qwen3 Yang et al. (2025a) to generate 200 text prompts describing the content target videos, covering a broad range of commonly encountered real- world scenarios. The style images used identical those described in the previous section. As no ground-truth videos are available, we adopt four evaluation metrics from VBench Huang et al.) 2024): Dynamic Quality, Motion Smoothness, Background Consistency, and Imaging Quality, to
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IHC (LPIPS +-003))
Extra Refs (SC -0.14)
EOG +-0.11)
Style
Style
Ref.
Content
Content
Content
Figure 7:
Ablation study of FieeViS. The upper images are generated from the full model.
assess the visual quality of the generated videos. Additionally, CLIP-Ter Text similarity is employed to evaluate video-prompt alignment. For assessing style consistency and fidelity, we report the CSD Score and FID, which measure the similarity between video frames and style images. Human pref- erence is also employed to assess the prompt alignment, stylization quality, and visual plausibility. We compare our approach against two state-of-the-art baselines Liu et al. (2024a); Ye et al. (2025): StyleCrafter and StyleMaster. For stylized T2V generation, we first generate base videos using W Wan et al. (2025), followed by applying Free ViS for video stylization.
Results. The qualitative and quantitative results for stylized video generation are presented in Fig- ure 6 and Table 2 StyleCrafter effectively transfers style into the generated videos; however, its outputs often lack dynamic realism and occasionally fail to align with the input prompts. Since the camera and object motions of the generated videos from StyleCrafter are relatively small (see the comparison results in the supplemented video), the calculated consistency scores are higher than those of other methods. In contrast, StyleMaster better adheres to the textual prompts but exhibits limited stylization capability. The combination of Vis and the Wan model achieves the best trade-off between stylization fidelity and content alignment, producing videos that are both visually compelling and semantically accurate, as reflected by the highest human preference scores. Note that the stylization performance Fiee inherently upper-bounded by the underlying image style transfer method it employs.
4.3 ABLATION STUDY
Free ViS primarily comprises three key components: IHC for layout reconstruction, Extra References for enforcing style consistency, and EOG for texture preservation plain areas. The effectiveness of each component is illustrated in Figure 7 Without IHC, the model fails to accurately reconstruct detailed scene layouts, leading to structural artifacts, as evidenced by incorrect roof reconstruction. Incorporating additional reference frames helps maintain consistent style features throughout the video, significantly enhancing visual coherence. the example, the last frame shows only a color shift and struggles to convey stylistic textural details. Furthermore, the EOG module mitigates the loss of style textures in visually homogeneous regions of the original video, thereby improving flow consistency and overall stylization quality.
5 RELATED WORK
Video Diffusion.
Diffusion-based video generation is advancing rapidly. with significant progress
in quality Agarwal et al. (2025); Yang et al. (2025c); Blattmann et al. (2023). efficiency Xi et al. (202)) Zhang et al. (2025a): Kahatapitiya et al. (2024), controllability Zhang et al. (2025b): : Bai al. (2225a) Ren et al. (025), and extrapolation Zhao et al. (2025): Dalal et al. (005); ; Mao et al. 2024). Unlike early baetd architectures Blattmann et al. (2023): Guo et al. (2024) Mei
& Patel (2023), which employ separate layers model spatial and temporal dependencies, recent
advances Kong et al. (2024); Yang et al. (2025c); Wan et al. (2025) adopt the full self-attention layers of DiT Peebles & Xie (2023) to jointly capture lexel-level coherence.
Video Editing. Leveraging the strong priors of video diffusion models, recent editing methods have significantly outperformed earlier approaches based on optical flow Xu et al. (2019), GANs Tzaban
et al. (2022); Frühstück et al. (2023), and image diffusion Ceylan et al. (2023) Geyer et al. (2024):
Feng et al. (2024). Unlike image editing, which mainly enforces spatial coherence Brooks et al. (2023a b). video editing additionally requires preserving temporal consistency Yang al. (2025b).
Prior work has addressed diverse tasks such as inpainting [Bian et al. (2025); Guo et al. (2025),
content swapping Liu et al. (2024h): Tu al. (2025): Zhang al. 2022cc), and interactive control

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Published as a conference paper at ICLR 2026
Deng al. (2024); Ma et al. (2025).
VACE Jiang al. (2025) proposes unified framework for video generation and editing with a mask-guided guided condition unit, while Any Ku et al. (2024) and
12VEdir(Oyynng et al. (2024) leverage the first edited frame guidance for pre- pre-trained I2V models.
Style Transfer. Neural style transfer Gatys ct al. (016)) has progressed with deep architec- tures Huang & Belongie 2 Liu et al. (2021): y Deng al. (2022) and the generative mod- els Azadi et al.] (2018): Zhang et al. (2023) Wang et al. (2023) Diffusion models further im- prove semantic alignment between style and content Wang al. (2024b); Xing et al. (2024), in- spiring training-free methods Wang et al. (2024a); Rout et al. (202);; Chung et al. (2024); Xu el al. (0255b that surpass traditional techniques. By contrast, video stylization remains underexplored. ViSt3D Pande & Sharma (2023) employs 3D CNNs for temporal consistency, StyleMaster Ye et al. (2025) finetunes video diftusion model with CLIP-guided cross-attention, and StyleCrafter Liu et al. (2024a) enables style- condiconditioned text-to-video generation. Nonetheless, these approaches still lag behind image style transfer in quality, with temporal consistency being key challenge.
CONCLUSION
In this paper, we introduce a training-free video stylization framework, Free ViS, which successfully addresses the propagation errors inherent in prior works. Free leverages IHC to constrain the layout and motion. To enable additional reference inputs without requiring extra fine-tuning, we propose isolated attention with masking and dynamics injection. Furthermore, we propose the EOG strategy to preserve style textures in plain areas. Extensive experiments are conducted to validate the effectiveness of our design and the superior performance of Free ViS over other methods.
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Inversion
Inversion
Reconst.
Reconst.
Editing
Editing
Figure 8: Latent movement along the inversion and denoising process for reconstruction and edit-
ing. Left: Conventional Inversion; Right: PnP Inversion. Purple arrows refer to the compensation.
A APPENDIX
A.1 PRELIMINARY: PNP INVERSION
Different from conventional inversion-based editing methods, PnP inversion Ju al. (2024) records the full denoising trajectory Xt, ,.. Xo (the target latents) during inversion, and explicitly injects the discrepancy between the reconstruction latent x and the target latent x, into both the reconstruction and editing latents x,. This compensation mitigates the accumulated error that is otherwise amplified by classifier-free guidance, thereby steering both reconstruction and editing back toward the original denoising trajectory (Figure
88. The update rule can be expressed as:
X < X + (x+ -0-- x(): x + x (x: - x;)
In this paper. we observe that the inverted noise alone is insufficient to guarantee the correct recon- struction of the layout and motion in modern video diffusion models, and PnP inversion (combined with our IHC strategy) can successfully resolve this issue.
A.2 IMPLEMENTATION DETAILS
Control signals such depth, Canny edges, and tile images offer additional content guidance for generative models and have been widely adopted in both training-free and training-based style trans- fer approaches Xu Xu al. (2025b); Xing et al. (2024); Wang et al. (2024a): Ye al. (2025). To enhance motion preservation, we adopt Wan2. 1-n.n-FFun- Fun-ool (0.5B) as our pretrained base model, an adapted variant of Wan2.1 Wan et al. (2025) that can incorporate depth as a control signal. This base model positions the reference tokens at the beginning of the visual token sequence and supports only single reference image, which serves the initial frame of the generated video. One can freely change the base model other video diffusion models, given the Free ViS framework. We utilize Euler Inversion with fixed-point iteration Xu et al. (2025a) to perform inversion under the Rectified Flow framework Lipman al. (2023). The number of iterations computational budgets. During inversion, we omit the reference latent and provide only the video depth map parsed by Video Depth Anything Chen et al. (2025) and concise text prompt extracted using Qwen2.5-Vi VI. Bai et al. (2025)). which describes the content and dynamics of the input video.
be adjusted based on the users'
A.3 COMPUTATION OF REFERENCE MASKS
Let video frames be indexed by / 0, ... T with pixel domain 24 C 7? We adopt RAFT Teed & Deng 2020) f IR22 + 2 to extract the forward optical flow from frame / to 1+1, and let S2 denote nearest-neighhor discretization to pixels.
I: 2
P. = P. P Pa + fp(p)) (A: s,.... t-1),
2 (p) Pt
(10)
where he-s denotes the forward propagation operator. Given target frame /. and source set S (O...., T-1). the set pixels in frame covered by computed by:
CSS)) = [1 (u)) : u Ωa. a a aes} s co
(11)
With two given frame indices and j (0 <0<1 < 1), the two novel-region masks are: (p 1.p & C,(()))
M)(P) 1[p ((0,,))) e 9z
(12)
This approach be extended references with pre-defined order.
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A.4 COMPUTATION OF FLOW MASKS
We construct a dense binary correspondence mask that records, for every source pixel in frame 8, the (discretized) terminal location it reaches in any target frame / when transported by optical flow. Let frames be indexed by / == 0,.... 7-1 with pixel domains 22 C z² As in $A.3 RAFT Teed & Deng (2200) provides forward flows f I2 = from k+11 (and analogously backward flows fg from k+ to k), and 11, _22 +22,, S2 denotes nearest-neighhor discretization onto frame t.
Intermediate sampling and terminal validity. Starting at p. n E R22, we propagate forward from to by sampling the flow at the nearest valid pixel and updating the continuous state:
P& 11x (ps),
Pk+1 P& fi(ss),
k == t-1,
e Φ Pr
(13)
The backward transport for t < defined analogously using fg::
Pk+1 11 (P&+1).
P = Pk+1 + fd((P+t),
==s-
$ f Pt (14)
Note that intermediate positions Ps are allowed leave the image domain; 11x(-) is used only to fetch subsequent flow values. We enforce validity solely at the terminal point: a trajectory from to t considered valid iff the continuous endpoint lies in S2
Bidirectional map and landing index. tional transport
Combining equation 13 equation
14
define the bidirec-
Ag c (u) (.->e(u) (u), 1 8,
(15)
t <<>
Given u E Ωg, its terminal continuous location is q A (u), and its discretized landing pixel is
v 1l((9) E 94
Xu= = 1/q E 24 E {3,
(16)
Pairwise mask and tensor stacking. The pairwise correspondence mask Ma-+ Ω x Q {0, 1} places single one at the discretized landing pixel when the terminal point is in in-bounds:
Ms-+e v) = Xu- I[y = v'],
u E Ω2 v E S4
(17)
Consequently, Ever, Ms-si (u, v) E 1) for every u, and the self-map reduces to the pixelwise identity: Ma-+o v) = 1[v = u.. Stacking Ma-r over all (s, t) yields the rank-6 tensor
Me (0, Xxxxxxxxxxx
Mis n. 1, x' = MG-re ((v, x), x²))
(18)
Remarks. In practice adopt nearest-n sampling via I-) in equation 13 equation 14 bilinear interpolation can be substituted without changing the definition in equation because the landing index is discretized by I-) Our construction matches the implementation choice that intermediate out-od- positions are permitted, while only the terminal in- bounds condition Xs-t is enforced. Alternatively, the optical flow module can be replaced with other correspondence estimation methods, such as DINOv3 Siméoni et al. (2025) features, for improved performance.
A.5 DYNAMIC-APPEARANCE DECOMPOSTTION FOR A CAUSAL VAE
The causal VAE in Wan Wan et al. 2025) encodes a video using a temporal subsampling factor producing fime-compressed latent sequence. We show that this video latent admits a decom- position into (i) an appearance term mainly determined by one frame per r-frame block and (ii) a dynamic residual. For r 4, the clip's appearance is therefore dominated by 1/4 of the frames.
Sampling and per-frame encoding. Given N-frame video V [V[)) and offset define the sampled frame sequence
Ap E (0. ..)..................... r).
S,VV = (V U (Virj + Apl = 0, 1,..., 1IN 10/-1)/r|-1 -)
Let Evid be the video encoder and Bing the same backbone in image (framewise) mode. The video encoder yields:
x(V) (Via(V)
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Best 27.74 dB
27.75
Video
27.30
27.00
Reconst.
26.75
Offset
(a) Optimal Offiset
(b) Appearance Transfer from Grayscale Video
Optimal Offset and visualization for appearance transfer. (a) The plot for reconstruction PSNR with different offsets; (b) The RGB video and its reconstruction via appearance transfer.
Figure 9:
The latents for sampled sequence obtained by encoding the sampled frames independently and stacking them along time:
(V. Ap) = (6img (1) (V,AA(
Because the sampled frames are independently encoded, a(V, Ap) only contains appearance infor- mation. Define the dynamic residual by:
dyy (V, Ap) =VV) aVV, Ap)
Appearance-dynantee exchange. For an RGB clip Vigb and its grayscale counterpart Virayy swapping appearance while preserving dynamics gives:
Vigns (A)) = (Zdya (V Ap) + a a(Vigb; Ap)),
(19)
gray (Ap) (Zdyn (Vrgby Ap) + a( aVrray Ap)),
(20)
where 2 denotes the VAE decoder. This implements "subtract source appearance, add target appear- ance" while keeping the dynamics fixed. Figurep--(b demonstrates that the appearance of the RGB video is successfully combined with grayscale dynamics, with only a minor color shift. This obser- vation inspires us inject the dynamic residual into the value matrices of static reference latents, which addresses the stuttering issue of additional reference inputs in Section 3.2
Choosing the offset. Ap selects which frame within each r-frame block anchors appearance. We choose it by minimizing coossreconsttruction error:
Ap" arg
min ((Vig((pp) Vegb) L(V (Ap), Vgray). Ape(0.....
where can be an +L+LPIS video reconstruction metric. As shown in Figure (a), the recon- struction performance is optimal when Ap 3 for r 4. Furthermore, the PCA visualizations of the video latents and the sampled frames exhibit close aligument (Figure IO). indicating that the temporal anchoring is consistent across the two representations. The additional references should be selected from SV, Ap), and the optical flow for inconsistency masking and EOG in Section 3.2 should be calculated accordingly for optimal performance.
A.6 DENOISING PROCESS WAN 12V MODEL
As in image diffusion models, the early denoising stages in video diffusion primarily establish the coarse global layout and motion of the generated video. while the later stages are dedicated to refining fine-grained details. As illustrated in Figure IT0 the primary layout and structure of the entire video are largely determined by around the 20th denoising step. later steps, the model mainly focuses the local appearance refinement. The PCA visualizations of the latents align well with our sampled frames with optimal offset.
A.7 NOISE INITIALIZATION
In recent diffusion- image and vidco editing works Hertz et al. (2022); Cao et al.] (2023); Parmar et al. (2023) Yang et al. (2025b): Li et al. (2024), noise inversion is prerequisite step for
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Noise
Step 10
Step 20
Step 30
Step 40
Step 50
Video
Figure 10: Denoising process of Wan 12V model. We apply PCA visualize the latents for different frames along the entire denoising process. The corresponding sampled video frames are given.
Reference Image
ouzz cat is walking towardts the samsera..
Frame
"A girl be., a diress welkking tiroughh Selo."
(a) Gaussian Noise
(b) Inverted Noise
Figure 11:
Effect of noise initialization for video stylization with reference. (a) The generated
videos share the same initial Gaussian noise. (b) Original video and its stylization via inversion.
Sop-48
Nerealladd (equerryn
Namailaad Prepurcc K2
familbud Prepanng 10
Horma add Taaqunnyy
Figure 12: Frequency spectrum of intermediate latents across timesteps. We apply Fast Fourier Transform (FFT) along the two spatial dimensions. Lower frequencies are concentrated near the center, whereas higher frequencies are distributed toward the corners.
essential content preservation when large-scale training is infeasible. However, we find that noise initialization alone is insufficient to capture the detailed motion and content for video stylization.
Gaussian noise widely adopted as standard initialization for generative models Ho et al. (2020): Lipman et al. (023).. Nevertheless, as shown in Figure [1 while the stylized reference image retains the overall semantic layout, is treated as an -ut-of-distribution example, and the video generated from the same Gaussian noise fails to exhibit realistic dynamic motion. In contrast, the noise obtained through inversion preserves critical dependencies across pixels [Staniszewski et al. (2024). Thus, employ inverted noise for video stylization, which enables partial recovery of camera and object motion; however, content not visible the first frame remains irrecoverable, and the color is inconsistent with the reference. Therefore, accurate motion and layout preservation in the stylized video.
seeking additional strategies for more
We further observe that the generated video content highly sensitive the reference, and replacing this frame with an edited or stylized one after inversion often introduces significant artifacts. We attribute this issue the strong attention coupling between the noise and reference latents during the entire denoising stages (Figure 2). Specifically, the noise latent estimated through inversion tends to overfit the distribution of the orginaa reference image. To mitigate this issue, we propose to remove the reference image during inversion, which substantially reduces these artifacts.
A.8 FREQUENCY ANALYSIS OF INTERMEDIATE LATENTS
In the frequency spectrum of the initially inverted noise, all frequency components exhibit similar intensity (Figure 12), characteristic of Gaussian white noise. As the denoising process progresses,
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the intensity of frequeeey (HF) components diminishes, while low-frequency (LF) components become more pronounced. This transition occurs most noticeably during the early stages. Therefore, our Indirect High- free-freyenccy Compensation (IHC) strategy is only applied in the early timesteps.
We observe that different frequency compo- nents of the intermediate latents serve distinct roles when applying PnP inversion during in- ference enhance content preservation. As Figure
buildins."
BIF vide
13 illustrates, the low-frequency (LF) components primarily govern the appearance attributes, such as the color distribution, of the generated video. frequency (HF) components predominantly en- code layout and motion information. Consis- tent with characteristics observed in natural im- ages, HF components capture edge and bound- ary details, which are critical for determining the structural layout. Since the HF component contains less color information, which will not affect the stylized results, we leverage it to constrain the layouts of the stylized videos in our method.
the contrary, the high-
Effects on video reconstruction of LF and HF compensations in PnP inversion.
Figure 13:
A.9 LIMITATIONS & FUTURE WORKS
Time Complexity. Similar to other training-free image & video editing approaches, FreeViS re- quires inversion process to obtain the denoising trajectory, which usually doubles the inference time. In the meanwhile, since FlashAttention Dao (2024) does not provide optimized support for masked attention, the use of masked attention in Section increases the overall time complexity. In practice, Free ViS adds approximately 30% overhead to the stylization time of the V2y imple- mentation. A straightforward workaround is use Free ViS for generating higinal--ttlizze video pairs prior to the supervised fine-tuning (SFT) of video diffusion model, thereby compensating for the scarcity of such paired data. Combining with recent advances of auto-regressive video diffusion models, real-time video stylization can be possible.
Stylization Upper Bound. The stylization quality of the references fundamentally constrained by the underlying image style transfer models. FreeViS enforces the pretrained base model to propagate style features from the reference frames across the entire video. However, the base model itself lacks the capability to explicitly parse interpret style features. As result, the overall quality of video stylization, both in terms of style similarity and content preservation, is fully determined by the chosen image model. As illustrated in Figures [4] and [5| different image-based models exhibit distinct stylization biases. Future work could explore a mixture-of-experts strategy to selectively combine their complementary strengths according to the style image or video content.
Optical Flow Estimation. Free ViS leverages optical flow to identify overlapping regions between reference frames in order to resolve temporal conflicts. However, conventional optical flow estima- tion methods, such as RAFFTTeee & Dengl (2020) used in this work, struggle with severe occlusion, leading temporal inconsistencies in the affected regions. More advanced flow estimation and occlusion- -cclusionhaaddlng techniques could be employed to mitigate this issue. Alternatively, dense and precise feature correspondences extracted with DINOv3 Siméoni et al. (2025) offer promising direction for constructing more reliable inconsistency masks.
A. FREEVIS WITH VARIOUS IMAGE STYLIZATION METHODS
Leveraging the strong prior of the diffusion model to accomplish high-quality image stylization has become mainstream in recent works [Chung et al. (024)): ): Xu et al. (2025b); Wang et al. (2024a). Fis utilizes the high stylization quality of these methods to accomplish video stylization. In this section, we show that different image stylization methods have different strengths, and Free ViS can be generalized to various methods and maintain their strengths. Three state-of-tee ofte--oftheeann aat image style transfer models are selected for experiments: StyleID Chung et al. (2024), StyleSSP et al. (0225)), and InstantStyle-plus Wang al. (202bb).
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Style & Video
Instant-plus
StyleSSP
StyleID
Figure 14: Video stylization results for ViS combined with different image style transfer mod-
els. "Instant-plus" denotes InstantStyle-plus Wang et al. (2024b).
Style & Video
pluss
Instant-plar
StyleSSP
StyleID
Figure 15: Video stylization results for Free ViS combined with different image style transfer mod- els. "Instant-plus" denotes InstantStyle-plus Wang al. (0024)..
As shown in Figures 14 and 15 InstantStyle-plus and StyleSSP achieve stronger high-level style alignment, whereas StyleID emphasizes low-level texture preservation. Among them. StyleSSP pro- duces smoother object surfaces and better layout reconstruction compared to the other two methods. InstantStyle-plut provides more favorable trade-off between visual plausibility and style preserva- tion. Since Free ViS is compatible with all these methods, the underlying image style transfer model can be flexibly substituted to meet the requirements of different scenarios.
A.11 QUANTITATIVE ABLATION STUDY
The quantitative results for the ablation analysis of each key component in FreeViS are presented in T Multi-reference integration is demonstrated to be the most effective module, with significant improvements in style preservation and temporal consistency. In Equation Out; merges the style information from multiple stylized references and contributes to the high-quality style propagation. Besides, Out, (Equation 66) leverages the spatial-temporal correspondence between patches from
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Stylization Quality
Video Consistency
Method
HP
CSD Score f
ArtFID L
FID J
LPIPS L
SC 1
MS f
FC +
ViSeViS
0.448
33.46
21.62
0.479
0.898
0.978
0.641
4.113
w/o IHC
0.449
33.49
21.60
0.482
0.899
0.971
0.879
4.021
w/o Ext. Refs
0.314
35.27
25.78
0.317
0.755
0.975
1.374
3.523
w/o BOG
0,409
33.89
22.16
0.463
0.886
0.974
0.753
3.973
Table Quantitative ablation study for FieeViS. "SC", "MS", "FC", and "HP" denote Style Con- sistency, Motion Smoothness, Flow Consistency, and Human Preference, respectively.
Training-Free
Training-
Complexity
FreeViS
Any
Any
TokenFlow
12VEdit
VACE
Time (min)
4.47
3.41
11.75
1.52
2.75
Space (GB)
13.56
8.38
45.11
29.24
44.53
22.14
Complexity Analysis of FreeViS and other baselines. Existing training-free stylization methods require additional inversion to obtain the initial noise. The inversion time has been counted in this table (1.44 mins for FieeViS). The lengths of evaluation videos are 81 frames (5 seconds). The runtime for 12VEdit is omitted since it requires inference-time training (more than hour). FreeVis and Any V2V* share the same base video diffusion model. Note that our Any V2V* implementation replaces the attention transfer in its original setting by {q, k} transfer, since the tention maps are too large, causing OOM issues.
Table 4:
the reconstruction branch to define the layout information, thereby improving the video consistency. Note that the patch correspondences from the reconstruction branch are close to the original video, since the noise latent of the reconstruction branch is directly compensated by PnP inversion.
The functions of Indirect High-frequency Compensation (IHC) and Out are relatively similar. Both of them obtain spatial information from the reconstruction branch (or the target latent) to constrain the content structure in the stylized videos. IHC further improves the robustness of content reconstruction for Free ViS in some complex cases that involve significant content changes across video frames, where the inverted noise and Out, struggle to model the layout, evidenced by the more precise content structure reconstruction and better flow consistency.
Explicit Optical-flow Guidance (EOG) leverages the optical flow extracted from the original video to compensate for the patch correspondence enrors in low-saliency regions across temporally distant frames. Since EOG alleviates the local style texture diminishing issue, the overall style patterns of the stylized videos follow the references style consistency and similarity (CSD Score). More results on the complex texture preservation performance of Free are presented in Figure 17
great extent, with noticeable improvements in
A.12 COMPARISON OF MODEL COMPLEXITY
Training-free video editing methods, which typically require an additional inversion process to initi- ate noise, often deliver slower inference speeds than training-bass bbaed methods. VACE and TokenFlow (based image diffusion models) present fast inference speed but struggle to stylize the video Ffigure4.. Due to the manipulation of extra references, FreeViS requires more runtime and memory usage than Any V2V*, shown in Table 4 With slight sacrifice in computational complexity (a 30% increase in inference time), Free ViS achieves significant improvements in stylization quality.
A.13
TUNING
Multi-Reference Selection. FieeViS leverages multiple stylized references to address the style propagation issue of existing methods. As we discussed in Section A.7 the diffusion model alone cannot effectively transfer the style patterns from the first reference to the subsequent frames with new content emerging, especially when the camera movements are significant. Given this observa- tion, we introduce the multi-reference integration strategy. Since existing video diffusion models only process short videos, e.g., 5 seconds (81 frames), the combination of the first and the last frames is able to cover most of the video content. However, this naive implementation shows weak robustness in complex videos, e.g., camera moving forward and backward. Therefore,
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Tin Cooeplotty Crrse
Retvracc (Madde)
(3)
(c)
Figure 16: Hyperparameter tuning for Vise (a) Improvements of style consistency 1 over the number of middle references (first and last references are included default). The inversion time not counted in the time complexity; (b) Trade-off of content preservation (LPIPS with content frames) and style similarity (FID 4 with style images) with the tuning of B; (c) The effect of y on Style Consistency f and Flow Consistency 4.
also include the middle frames as references, Specifically, we uniformly sample the middle frames along the video for more references, where the sampling density defined by the number of middle references. Note that the sampled middle indices should follow the offset discussed in Section A.5
As shown in Figure 16 (a), including the last reference (0 middle reference) significantly improves style consistency from 0.755 (see Table 3) to 0.864, and adding a single middle reference further elevates this score to 0.898 with slight increase runtime. Note that more middle references improve the robustness of the stylization, but increase the possibility of destroying large style pat- terns, leading to a slight decrease in style preservation. Empirically, we find that when this middle reference position is at f//2 for video with length f. the model achieves the highest stylization quality. The current version of FreeViS adopts a uniform reference sampling strategy, which can be improved to adaptive strategy based changes in video content across frames. We leave this possible improvement for future studies.
Tuning, In equation 8 3 controls the strength of reconstruction-guis guided generation. This strategy can be seen as another form of IHC, as the reconstruction attention maps define the spatial-tempon correlation between pixels in later denoising stages (see figure 2 and can be utilized to indirectly force the stylized video to follow the layout or motion of the original video. However, if this cor- relation is too strong (8 is too large), the stylization branch will produce the same semantics as the reconstruction branch in the early denoising stages, leading to the disappearance of stylization fea- tures. Also, the newly introduced style features do not strictly adhere the temporal or geometric layouts of the original videos. Strong reconstruction forcing will cause stylization failures, e.g., missing style textures. Figure (b) shows the trade-off between content similarity and style fi- delity with the change of B. Based on the visual examination, we observe that the highest stylization quality (best trade-off between LIPIS and FID) is accomplished when 3 E [0.3, 0.4].
Tuning. The EOG module constrains the fros--ramme attention areas to compensate for patch cor- respondence errors in low-saliency regions across temporally distant frames. As in the early denois- ing stages (first 60% timesteps), the diffusion model requires global information for low- wowfrrquency semantic generation, we only apply the EOG strategy in the final stages (last 40% timesteps), where the model primarily focuses local texture refinement (Figure TOp. 7 defines the strength of this optical-flow guidance in FieeViS. We observe consistent improvements in style preservation as ? increases. However, when y exceeds 0.4, there noticeable localized, blocky jitters in the styl- ization videos, leading to worse flow consistency. We attribute this issue to the disconnection from surrounding patches in the attention operation inside EOG, Empirically, 7 0.2 delivers the best temporal consistency with relatively high-quality style preservation.
A. 14 MORE RESULTS WITH COMPLEX STYLES
ViS has the capability of texture preservation, which enables high-quality video stylization with complex styles (emphasizing the strokes). Figure 17 demonstrates the qualitative performance of Fiee on two classic style images commonly used in recent image style transfer works. The experimental results show that FreeViS successfully preserves local textures during camera and object movements. Please check more results in the supplementary video.
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Style & Video
Stylization
Style & Video
Stylization
Figure 17: Video stylization results for FieeViS with two classic style images. InstantStyle-plus provides the stylized references for each video. Both videos involve camera and object movements.
Local textures are cropped out for better examination (zoom in to check the texture preservation).
Video & GT
Free
wio EOG
Figure 18: Effect of EOG for texture preservation along object and camera movements. The stylization ground truth (GT) is cropped from the reference image.
Cutoff Freq.
0.4
0.3
0.2
0.15
0.1
0.01
CSD Score f
0.4493
0.4489
0.4481
0.3791
0.264
0.1134
Flow Consistency
0.762
0.711
0.641
0.539
0.415
0,242
Table 5: Ablation study for the selection of the cutoff frequency in IHC. This cutoff frequency is
defined as circle around the origin (center), so noise components outside this frequency are passed through the high-pass filter. In this table, from left to right, the stylization latent receives increasing compensation from the target latent (original video).
A.15 MORE RESULTS WITH EOG
To further demonstrate the effectiveness of FOG for texture preservation, provide additional qualitative results in Figure 18 The texture-diminishing issue in low-saliency areas along the object or camera movement alleviated by EOG, as evidenced by sharper, finer-grained textures.
A.16 CUTOFF FREQUENCY IN IHC
The cutoff frequency of the high-pass filter (HPF) in the proposed IHC module is critical. We observe that if this frequency is too small, the stylized video will be directly dragged back towards the original video, indicated by low style scores and good flow consistency. On the contrary, if the
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Video
Free
Any
A2V
12VEdit
Figure 19:
Qualitative comparison of FiseViS with other reference-based video editing methods. All the methods are provided with the same first reference.
frequency is too high, information is passed through the HPC. Empirically, set this frequency to be 0.2 for the best trade-off.
A.17 MORE RESULTS ON VIDEO STYLIZATION
In the main paper, we omit the results of Vny Ku al. (2024) and 12VEdit Ouyang al. (2024). For completeness, their results are provided in Figures 19 and 20 Both methods fail to faithfully reconstruct the essential layout of original video and exhibit the previously discussed propagation errors, primarily due to their reliance single reference input. Any V2V* (our re- ce-imllementation of Any V2V on the Wan model) demonstrates improved layout preservation. contrast, Free ViS effectively addresses the propagation error and achieves the highest stylization quality.
A.18 MORE RESULTS STYLIZED T2V GENERATION
Consistent with previous observations, StyleCrafter Liu et al. (2024a) demonstrates strong style preservation struggles with prompt fidelity and motion diversity. In contrast, StyleMaster Ye al. (202)) achieves better text-video alignment but fails to effectively transfer the target style. The combination Free ViS with the Wan model provides the best balance between style preservation and prompt fidelity, yield- ing results that are also more visually plausible.
Additional results stylized video generation are shown in Figure 21
A. FREEVIS WITH VARIOUS VIDEO DIFFUSION MODELS
To further validate the compatibility of FreeViS, we implement on another pretrained I2V model, Hunyuan Video- 12V Kong et al. (2024). Owing to its token replacement mechanism, the first refer- ence latent is tightly coupled with the latents subsequent frames, which leads to severe overfitting during inversion. As result, standard inversion fails to reconstruct the video and introduces substan- tial artifacts. In contrast, PnP inversion with compensation proves both more effective and essential, ensuring faithful video reconstruction. We present qualitative comparison of Free implemented on Wan Wan et al. (2025) and Hunyuan Video in Figure 22 In most cases, the two models achieve comparable stylization performance, while the Wan model demonstrates superior robustness and
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Video
Free
Any
Any
12VEdit
Figure 20:
Qualitative comparison of ViS with other reference-based video editing methods. All the methods are provided with the same first reference.
temporal consistency. These results highlight the importance of leveraging the strong prior of an advanced video diffusion model to achieve higher stylization quality with Fise
A.20
USE OF LLMS
The authors first wrote the content of this paper and then gave it to LLM for polishing. The polished content was selectively used to replace the original content, primarily in grammatical aspects.
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eyelist is pedaling uphill on winding mountain road.
StyleMaster
Style
Free Wan StyleCrafter
Smoke slowly rising frome chinney in silent mountain village.
Style
StyleMaster
FreeViS-Wan StyleCrafter
Figure 21: Qualitative comparison of FieeViS with other stylized video generation frameworks.
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Video
Wan 2,1
Hunyuan Video
Video
Hunyuan Wan 2.1
Figure 22: Qualitative comparison of Fiee combined with different base models: Wan Wan
al. (2255) and Huanyuan Video Kong et al. (2024). Their stylization results in most cases similar, while Wan presents smoother reconstruction and better temporal consistency. The third row shows a failure case of Huanyuan Video in consistency preservation.
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Reviews:
[{"review_id": "aIvmyHmGyW", "reviewer": "Reviewer_NmoF", "summary": "The paper introduces FreeViS, a training-free video stylization framework that addresses the challenges of temporal consistency and style richness in video generation. Instead of relying on paired video data or fine-tuning, FreeViS integrates multiple stylized reference images into a pretrained image-to-video (I2V) model. Its design incorporates indirect high-frequency compensation to refine content layout and motion fidelity, multi-reference integration to reduce flickering and propagation errors, and explicit optical-flow guidance to maintain style textures in low-saliency regions. The authors claim that FreeViS outperforms recent baselines in both stylization quality and coherence.", "questions": "1. Could you clarify how multiple stylized references are used in practice? \n2. How does the method handle inaccurate optical flow, especially in fast-moving scenes or occlusions? \n3. Have you tested FreeViS on more diverse styles with texture details (e.g. oil painting with strokes) or content with texture details (e.g. hair)? If so, how does it perform in terms of fidelity and consistency?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Originality: The idea of combining inconsistent stylized references with a pretrained I2V model in a training-free manner is practical and relevant, especially for low-resource settings.\n2. Quality: The method is well-motivated and shows promising results in terms of stylization fidelity and temporal coherence. The integration of high-frequency compensation and flow cues is conceptually sound.\n3. Clarity: The paper is generally easy to follow, with clear motivation and methodology. Figures and examples help illustrate the core ideas.\n4. Significance: FreeViS addresses a real-world need for efficient and high-quality video stylization without the burden of training, making it potentially impactful for content creators and researchers.", "weaknesses": "1. Limited Novelty: Several components (e.g., AdaIN, cross-frame attention, flow-based guidance) have been used in prior works like Style Injection in Diffusion, StyleID, and Text-to-Video Zero. The novelty lies more in the integration to video diffusion model than in individual techniques.\n2. Unclear Use of Multiple References: Although the paper claims to use multiple stylized references, the experiments and visualizations consistently show only a single style image. There is no explanation of how multiple references are selected, fused, or weighted.\n3. Insufficient Evaluation of Key Components: The Indirect High-Frequency Compensation is mentioned but not thoroughly analyzed. The ablation image lacks accompanying discussion, making it hard to assess its contribution.\n4. Optical Flow Reliability: The use of flow in low-saliency regions is promising, but flow errors can degrade detail preservation, especially for moving objects. Figure 7 shows only one example, and it’s unclear whether it involves motion.\n5. Style Diversity: The method is tested on relatively simple styles. It’s unclear how well it generalizes to complex or abstract styles with texture details.", "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": "olht7lwo5V", "reviewer": "Reviewer_razj", "summary": "This paper proposes FreeViS, a training-free video stylization framework that aims to solve temporal inconsistency (flickering) in frame-by-frame methods and propagation error in single-reference I2V-based methods. The key innovation is integrating multiple stylized reference frames into a pretrained I2V model through three main technical components: 1. Indirect High-Frequency Compensation (IHC) that preserves layout/motion while maintaining stylized colors, 2. an isolated attention mechanism with dynamic injection and optical flow-based masking to handle inconsistent references, and 3. Explicit Optical Flow Guidance (EOG) to preserve textures in low-saliency regions. Experiments demonstrate improvements over existing methods in both stylization quality and temporal consistency.", "questions": "- Can you provide ablation studies on hyperparameters β and γ? How is the \"final stage\" for γ activation defined?\n- Can you provide inference runtime comparisons with baselines?\n- Can you justify the choice of 3 reference frames and provide ablations with different numbers/positions of references?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The IHC mechanism is well-motivated by frequency analysis showing high frequency components encode layout/motion while low frequency components control appearance.\n- Solve the multi-reference challenges, dynamic injection addresses stuttering, flow-based masking resolves inconsistencies.\n- The qualitative results in Figures 1, 4, 5, 6, 16, and 17 are very convincing and show a clear improvement in style propagation and temporal consistency over baselines.\n- The quantitative evaluations and ablation study  are comprehensive and support the authors' claims.\n- Clear improvements over baselines in both video-to-video stylization and stylized text-to-video generation tasks", "weaknesses": "- Missing hyperparameter ablation studies and analysis, the method combines three attention modes using hyperparameters β and γ (Eq. 8), but no ablation or analysis is provided. How sensitive is the final video quality to these parameters? Additionally, γ is only non-zero in the \"final stage\" but this stage is not clearly defined, what threshold determines this?\n- No inference runtime comparisons with baselines are provided. This makes it difficult to assess the practical computational cost relative to quality gains.\n- Reference frame selection not justified or ablated. The paper uses first, middle, and last frames, calling this an \"empirically effective choice\" without systematic study. Why specifically these 3 frames? How does this compare to different numbers of references? What is the performance vs. efficiency trade-off with more references?", "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": "RxrtlQAd0u", "reviewer": "Reviewer_HBzJ", "summary": "This paper presents FreeViS, a training-free video stylization framework that leverages a pre-trained image-to-video model, multi-reference integration, and high-frequency compensation to generate stylized videos with rich details and strong temporal coherence.", "questions": "1. Could you provide more details on how the system handles potential errors from the optical flow computation? Specifically, are there any mechanisms to correct or compensate for flow inaccuracies to prevent visible flickering in the final output?\n\n2. Regarding Equation 2, how was the cutoff frequency for the low-pass filter determined? Could you discuss the sensitivity of the final results to this parameter and whether an ablation study was conducted to guide its selection?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The main innovations are:\n1. Training-Free Multi-Reference Integration: A novel pipeline that ingeniously integrates multiple stylized references into a pre-trained I2V model, effectively suppressing error propagation and eliminating flickering without any fine-tuning.\n\n2. High-Frequency Compensation for Layout & Motion Preservation: A dedicated mechanism that utilizes high-frequency signals to constrain content structure and motion dynamics, ensuring superior temporal consistency.\n\n3. Flow-Based Texture Preservation: The innovative use of optical flow motion cues to actively maintain and preserve style textures, enhancing the overall style richness and visual quality.", "weaknesses": "1. The proposed method's robustness is intrinsically tied to the accuracy of the optical flow algorithm. Inaccuracies in flow estimation are likely to propagate through the system, potentially leading to residual temporal flickering, which represents a fundamental limitation of the current framework.\n\n2. A significant concern is the ambiguous contribution of the base model versus the novel components. The observed temporal coherence in the stylized videos may be predominantly attributable to the inherent strong temporal consistency of the underlying video generation model, rather than the proposed technique itself. This makes it difficult to isolate and validate the actual improvement brought by the authors' method.", "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": "FsZcWuE2M9", "reviewer": "Reviewer_nCHs", "summary": "This paper proposes FreeViS, a training-free video stylization framework that effectively addresses the propagation issues in reference-based stylization. The key idea is a dual-branch design that incorporates high-frequency compensation, additional inconsistent references, and optical-flow-guided masked attention. The method can be applied to both image-based V2V stylization and stylized T2V generation, demonstrating higher stylization fidelity and competitive temporal consistency compared to prior training-free approaches.", "questions": "- Why is the video consistency score in Table 1 lower than baselines? Why is style consistency in Table 2 weaker than StyleCrafter? A brief explanation in the paper would improve the reader’s understanding.\n- How much slower or heavier is the dual-branch pipeline compared to comparison method? Please provide generation time and VRAM usage comparisons.\n- How does performance change when sampling references more or less frequently? Does the choice of image stylization method for reference frames affect results? A sensitivity analysis would strengthen the claims.\n- How do other metrics change across ablations, beyond the single metric currently reported? Showing full-metric ablation tables will better support the effectiveness of each component.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies and analyzes the root causes of temporal artifacts in existing training-free stylization, and proposes a well-motivated dual-branch design to address them.\n- As a training-free framework, the method is practical.\n- The method naturally extends to both video-to-video stylization and stylized text-to-video generation, which increases practical value.\n- Compared with existing baselines, the stylized outputs show richer texture, stronger style faithfulness, and fewer propagation errors, supported by convincing qualitative comparisons.", "weaknesses": "- While qualitative results are strong, the quantitative metrics in Table 1 and Table 2 are not consistently better than competing methods.\n- The ablation study is limited, showing only a single metric and lacking a broader evaluation across other metrics.\n- The dual-branch pipeline leads to higher memory usage and longer generation time, yet computation efficiency is only briefly mentioned in the appendix. A quantitative comparison of VRAM usage and runtime is needed.\n- Several design choices, such as reference sampling frequency, number of references, and optical-flow strength, are not thoroughly analyzed. A sensitivity study would make the method's robustness and design justification clearer.", "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)