RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Authors: Min Zhao
Guande He
Yixiao Chen
Hongzhou Zhu
Chongxuan Li
Jun Zhu
Venue: ICML 2025 poster (2025)
Keywords: video diffusion transformers; video diffusion model; length extrapolation
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: RIFLEx offers a true free lunch—achieving high-quality $2\times$ extrapolation on state-of-the-art video diffusion transformers in a completely training-free manner.

Abstract: Recent advancements in video generation have enabled models to synthesize high-quality, minute-long videos. However, generating even longer videos with temporal coherence remains a major challenge and existing length extrapolation methods lead to temporal repetition or motion deceleration. In this work, we systematically analyze the role of frequency components in positional embeddings and identify an intrinsic frequency that primarily governs extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet effective approach that reduces the intrinsic frequency to suppress repetition while preserving motion consistency, without requiring any additional modifications. RIFLEx offers a true free lunch—achieving high-quality $2\times$ extrapolation on state-of-the-art video diffusion transformers in a completely training-free manner. Moreover, it enhances quality and enables $3\times$ extrapolation by minimal fine-tuning without long videos.

RIFLEx:
A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Min Zhao Guande He²
Yixiao Chen 13
Hongzhou Zhu 13 Chongxuan Li456 Jun Zhu 137
Abstract
2023; Bao et al., 2023), which combines the scalability of diffusion models (Sohl-Dickstein et al., 2015; Ho al., 2020; Song et al., 2021) with the expressive power of trans- formers (Vaswani, 2017).
Recent advancements in video generation have en- abled models synthesize high-quality, minute- long videos. However, generating even longer videos with temporal coherence remains major challenge and existing length extrapolation meth- ods lead to temporal repetition or motion deceler- ation. this work, we systematically analyze the role of frequency components in positional em- beddings and identify an intrinsic frequenyy that primarily governs extrapolation behavior. Based this insight, we propose RIFLEx, minimal yer effective approach that reduces the intrinsic frequency to suppress repetition while preserving motion consistency, without requiring any addi- tional modifications. RIFLEx offers a true free lunch-achieving high-quality 2x extrapolation state-of-the-art video diffusion transformers in completely training-free manner. Moreover, it enhances quality and enables 3x extrapolation by minimal fine-tuning without long videos. Project page and codes: https://rifeex-videe iiiiiiiieeiiiiee...ui..o..o..
Despite these advancements, generating longer videos with high-quality and temporal coherence remains fundamental challenge. Due computational constraints and the sheer scale of training data, existing models are typically trained with fixed maximum sequence length, limiting their ability to extend content. Consequently, there is increasing interest in length extrapolation techniques that enable models to generate new and temporally coherent content that evolves smoothly over time without training on longer videos.
However, existing extrapolation strategies (Chen et al., 2023b; bloc97, 2023; Peng et al., 2023; Lu al., 2024b; Zhuo et al., 2024), originally developed for text and image generation, fail when applied to video length extrapolation. Our experiments show that these methods exhibit distinct failure patterns: temporal repetition and slow motion. These limitations suggest a fundamental gap understanding how positional encodings influence video extrapolation.
To address this, isolate individual frequency compo- nents by zeroing out others and fine-tuning the target video model. We find that high frequencies capture short-term dependencies and induce temporal repetition, while low fre- quencies encode long-term dependencies but lead to motion deceleration. Surprisingly, we identify a consistent intrinsic frequency component across different videos from the same model, which primarily dictates repetition patterns among all components during extrapolation.
1. Introduction
Recent advances in video generation (Brooks et al., 2024:
Bao al., 2024; Yang et al., 2024; Kong et al., 2024; Zhao et al., 2023; Team, 2024b; Zhao et al., 2024) enable mod- els to synthesize minute-long video sequences with high fidelity and coherence. A key factor behind this progress is the emergence of diffusion transformers (Peebles & Xie,
Building on this insight, we propose Reducing Intrinsic Frequency for Length Extrapolation (RIFLEx), minimal yet effective solution that lowers the intrinsic frequency to ensure it remains within a single cycle after extrapolation. Without any other modification, it suppresses temporal repe- tition while preserving motion consistency. As byproduct, RIFLEx provides principled explanation for the failure modes of existing approaches and offers insights that natu- rally extend to spatial extrapolation of images.
'Dept. of Comp. Sei. & Tech.. BNRist Center, THU-BosE ML Center, Tsinghua University. 'The University of Texas at Austin. "ShengShu. "Gaoling School of Artificial Intelligence Renmin Uni- versity China Beijing, China. 'Beijing Key Laboratory of Re- search on Large Models and Intelligent Governance. 'Engineering Research Center of ommendation, MOE. 'Pazhou Laboratory (Huangpu). Correspon- dence to: Chongxuan Li <chongxuanli @ wor..ceu. en>>, Jun Zhu <deszj @ wiinghua.cdu.cn>>
Intelligent Search and Rec-
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
Extensive experiments on state-of-the-art video diffusion transformers, including CogVideoX (Yang al., 2024)

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
MOD REG SER SE 600
(a) 2x temporal extrapolation from 129 to 261 frames.
(b) From 480 720 to 960 1440.
(c) 2x temporal and spatial extrapolation from 480 720 x 49 to 960 1440 x 97.
Figure 1. Visualization of RIFLEx for temporal, spatial, and combined extrapolation. Our base models are (a) Hunyuan- Video (Kong al., 2024) and (b-c) Cog VideoX-5B (Yang al., 2024), where we do not use any videos longer or larger than those used for pre-training. More demos and prompts used this paper are listed Appendix and the supplementary materials, respectively.
and Hunyuan Video (Kong et al., 2024), validate the effec- tiveness of RIFLEx (see Fig. 1). Remarkably, for 2x ex- trapolation, RIFLEx enables high-quality and natural video generation in completely training-free manner. When fine-tuning is applied with only 20,000 original-length videos--rquirrng just 1/50,000 of the pre-training com- autation-sample quality is further improved, and the effec- tiveness of RIFLEx extends to 3 extrapolation. Moreover, RIFLEx can also be applied the spatial domain simultane- ously extend both video duration and spatial resolution. Our key contributions summarized as follows:
sion transformers in a completely training-free manner. Moreover, it enhances quality and enables 3x extrapo- lation by minimal fine-tuning without long videos.
2. Background
2.1. Video Generation with Diffusion Transformers
Given a data distribution Pdata. diffusion models (Sohl- Dickstein et al., 2015; Ho et al., 2020; Song et al., 2021) progressively perturb the clean data Zo ~ Pdata with a transition kernel gejo (xx (/o))
Naa of I), i.e., æt a + o.E, where E [O. az dt are pre-defined noise schedule, and e (O, I) is Gaussian noise. Under proper designs of a,, or, the distribution of ær is tractable, e.g., standard Gaussian.
We provide comprehensive understanding of video length extrapolation by analyzing the failure modes of existing methods and revealing the role of individual frequency components in positional embeddings.
A generative model is obtained by reversing this process from = T to O, whose dynamic is characterized by the score function Vz, log (2:)) The score function is usually parameterized by a neural network s0(x,, t) and learned with the denoising score matching (Vincent, 2011): (w (w()))s () Vz, log qeo (2
We propose RIFLEx, a minimal yer effective solution that mitigates repetition by properly reducing the in- trinsic frequency, without any additional modifications.
RIFLEx offers a true free lunch lunch-achieving high- quality 2x extrapolation on ofate-of-the-ae the-art video diffu-
2

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RIFLEX: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
where w()) is weighting function. The de facto approach for modeling video data via diffusion models is first code the video data into sequences of latent space, then perform diffusion modeling with transformer-bent based neural network (Peebles& & Xie, 2023; Bao et al., 2023).
generalization ability of the network and the positional en- coding. Whereas Position Interpolation (PI) (Chen et al., 2023b) uniformly down-scales all frequencies in RoPE em- bedding to match the training sequence length. In specific, the new RoPE frequencies are calculated as g = 0 /8, where & = L' L, and L, L' is the sequence length for train- ing and inference, respectively.
2.2. Position Embedding in Diffusion Transformers
A position embedding is fixed or learnable vector-valued function f that maps a n-axes position vector p E No to some representation space. This position information can be incorporated into transformers through various mech- anisms, such as through additive (Vaswani. 2017; Raffel et al., 2020; Press et al., 2021) or multiplicatiie (Su al., 2021) operations with other input or hidden embeddings.
A key limitation of both PE and is their reliance on train- ing at the target sequence length, otherwise, the performance degrades drastically. To address this, NTK-Aware Scaled RoPE (NTK) (bloc97, 2023) combines the ideas of both position extrapolation and interpolation. Specifically, NTK adjusts the base frequency b for all dimensions as:
Rotary Position Embedding (RoPE) (Su et al., 2021) has emerged as the predominant method in transformers. RoPE encodes relative positional information by interacting with two absolute position embeddings within the attention mech- anism. Specifically, for sequence indexed by a single axis (i.e. 1), given input x E IR with position p E N_, RoPE maps it to an absolute-posiae -ooiuttt--piiionn encoded embedding on I2d with d' S d, i.e.,
@ (Ab) (Ab)- x = ad ((d' 2)
122
(2)
where s = T./L.. NTK effectively applies PE for high frequencies and PI for low frequencies, enabling training- free extrapolation. (bloc97, 2023; Zhuo et al., 2024).
YaRN (Peng et al., 2023) introduces fine greingr base fre- quency adjustment strategy. It first categorizes all frequen- cies into three groups based on the number of cycles elapsed over the training length, defined rj (2T) L0g. Given two pre-determined thresholds 8 with ra'/2 S a < B S Ti. YaRN adjusts the RoPE frequencies as:
R Pr 0)5-
cos p0; sin p0,
sin p00]] cos cosp0, x2]+1]
where I E R2 with f, = b D-20-13//d for j 1..... d/....d//2. Here, represents the frequencies for all di- mensions of the RoPE embedding and b is a hyperparameter that adjusts the base frequency. It can be verified that the dot product between two RoPE-embele eebeeddddd vectors encodes the relative positional information between them. In practice, RoPE is applied to the query and key vectors before the dot product operation in the attention mechanism, and thus the result attention matrix encodes the relative positional information.
@YaRN (r,)0, + (1 (r;)) 2 j 1,
d' d/22.
1,
ifr,> B,
(3)
T(55) =
0,
if < a,
otherwise,
In practice, YaRN exhibits better training-free extrapola- tion performance compared to NTK and can achieve great performance with a relatively small fine-tuning budget on target length (Peng et al., 2023).
RoPE with Multiple Axes. RoPE can be extended to multi- axes position vector E N for n > 1. One popular practice is to encode each axis independently. For example, consider a video input æ E IR with diree-dimeniional co- ordinates (t, h, w), there are three axis-specific parameters 0², 0%, 0". Single-axis RoPE, as defined in Eqn. (I), is then applied separately along the feature dimension with these three parameters. The final multi-axes RoPE is obtained by concatenating the three single-axis RoPE embeddings.
Length Extrapolation in Image Diffusion Transformers. Image diffusion transformers have two key characteristics related to RoPE: (1) image data is represented as sequence with height and width axes, and (2) an iterative diffusion sampling procedure. These characteristics inform specific length extrapolation techniques for image diffusion models.
For multi-axes sequence, RoPE is independently applied to each axis. allowing length extrapolation techniques like NTK and YaRN to be used separately on height and width, termed Vision NTK and Vision YaRN (Lu et al., 2024b). For sampling, different RoPE adjustments can be employed at various diffusion timesteps. For instance, Time-aware Scaled RoPE (TASR) (Zhuo et al., 2024) leverages PI at large timesteps to preserve global structure while using NTK at smaller timesteps to enhance visual quality.
2.3. Lenghh Extrapolation with RoPE
In this section, we briefly recap the techniques for length extrapolation with RoPE adopted in text and image.
The most straightforward approach. Position Extrapola- tion (PE), extends the input sequence length without mod- ifying the positional encoding, which purely relies on the
3

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
2x length extrapolation
2x spatial extrapolation
Normal length
Video of 49 frames
Image of IK resolution
PE
(a) Temporal repetition
(d) Spatial repetition
PI
(b) Slower motion
(e) Blurred details
NTK
(c) Temporal repetition
(l) Spatial repetition
Visualization of existing methods for extrapolation video image generation. The base models Cog VideoX- SB (Yang et al., 2024) and Lumina-Next (Zhuo et al., 2024) are trained to sample videos of up to 49 frames and images of to IK resolution, respectively. Existing methods lead to temporal repetition or slower motion in vidco extrapolation and spatial repetition or blurred content in image extrapolation, respectively. Please refer to Appendix C for more results and details.
Figure
3. Method
image spatial extrapolation, revealing parallels to video.
Our goal to understand and solve the video length extrap- olation problem thoroughly. We first highlight the intriguing failure patterns of existing methods, analyze the role of dif- ferent frequency components in positional embeddings, and identify an intrinsic frequency. Based on this, we derive minimal solution that enables length extrapolation: reducing the intrinsic frequency. As byproducts, our method not only provides a principled explanation for the failure of existing approaches in video extrapolation but also offers insights applicable spatial extrapolation in images.
PE, which directly extends positional encoding beyond the training range, leads to lemporal repetition, causing videos to loop instead of progressing naturally (Fig. 2a). A similar phenomenon occurs in image generation, where spatial repetition occurs instead of generating new content.
2023b), which compresses po- sitional encodings within the training range, leads to slow motion by stretching frames over time (Fig. 2b). While this approach preserves structural coherence, it lacks temporal novelty. In image generation, this results in blurred details rather than new content (Fig. 2c).
Conversely, PI (Chen
3.1. Failure Patterns Existing Methods
As shown Fig.2c, NTK (bloc97, 2023) also induces tem- poral repetition, failing to generate meaningful motion pro- gression. In image generation, it causes spatial repetition (Fig. 2f). While other methods (Peng et al., 2023; Lu et al., 2024b; Zhuo et al., 2024) differ from NTK in implementa- tion, they invariably suffer from one or both of these two failure patterns: either motion decelcration or content repe- tition (see Appendix C for further analysis).
Although the term "extrapolation" widely used across dif- ferent domains, its role in video generation is fundamentally different from text and images. In video generation, the objective is to create new and temporally coherent content that evolves smoothly over time. In contrast, text extrapo- lation primarily extends the context window, while image extrapolation typically involves adding nigh-resolution de- tails rather than generating meaningful new content.
Beyond revealing these limitations, findings provide intuitive understanding of how positional embeddings fundamentally shape temporal motion, motivating our in- depth frequency component analysis in the next section.
As result, extrapolation strategies developed for text and images fail in video length extrapolation, exhibiting intrigu- ing failure patterns, illustrated in Fig. 2. To better under- stand these patterns, we also conduct the counterparts on

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Repeat times under 2x extrapolation
RoPE components
Dynamic & repetition behavior in training length
Franiey
4 times
1 2
Rapid changes accompanied by short-range repetitions
Frame
Framosy
2 times
1
Regular dynamics and no repetition
Framee,
Pramnep-1
No repetition
y. 01.5
Slow motion and no repetition
Figure Visualization frequency components and their roles video generation. High frequencies capture rapid movements short-term dependencies, inducing temporal repetition, while low frequencies encode long-term dependencies with slow motion.
3.2. Frequency Component Analysis in RoPE
Second, frequency components influence the perceived mo- tion speed in video generation. This effect correlates the rate of change in positional encoding between consecutive (e.g., p-th and (p + 1)-th) frames:
We begin by analyzing the role of individual frequency components in RoPE (Su et al., 2021). We follow the tation in Sec. 2.2 but focus on the time axis and omit the subscript t for simplicity. We isolate specific frequency components by zeroing out others and fine-tune the target model (Yang et al., 2024) on its training length to adapt to the modified RoPF. Two key insights emerge from this anal- ysis.
A cos((p 1)0,) cos
(6)
Higher frequencies (i.e., larger 0,) typically result in larger Aj, making the model more sensitive to rapid movements. Conversely, lower-frequent frequency components induce minimal positional encoding shifts between adjacent frames, favoring slow-motion dynamics, aligning with results in Fig. 3.
First, different frequency components 8, capture temporal dependencies at varying scales, dictated by their periods:
Given that each component has its own period Nj. a key question arises: which frequency primarily dictates the observed repetition pattern in length extrapolation?
2T N₂
(4)
where j denotes the frequency index in RoPE. As illus- trated in Fig. 3, when the frame interval exceeds N,, the periodic nature of the cosine function forces positional en- codings-and consequently, generated video content-to repeat. Given training length L,, the number of temporal repetitions can be quantified as:
We define the intrinsic frequency component as the one whose period N, is closest to the first observed repetition frame in a video, as determines the overall behavior:
k = arg min IN, - NI,
where j denotes the frequency index RoPE. Surprisingly, this intrinsic frequency remains consistent across different videos generated by the same model, despite slight varia- tions in N. For instance, k is 2 for Cog VideoX-5B (Yang et al., 2024) and 4 for Hunyuan Video (Kong et al., 2024) respectively, as detailed in Appendix E.
L0
rj
(5)
N, 2x
As shown in Fig. 3, when high-frequency component has ry 2, the video completes two cycles within the training length and four cycles during 2x extrapolation. contrast, a fww-frequnncy component with r, = 0.5 remains within a single cycle even when extrapolated.
In the rare case where a model exhibits inconsistent intrin- sie frequencies across videos, we suggest treating all such
5

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
x extrupolation without fine-tuning
Training-fres
Fine nre-ttuning
3x extrapolation without fine-tuning
NoRepeat
Dynamic Degree
Imaging Quality 1
Score
Consistency
3x extrapolation with fine-tuning
3x extrapolation comparisons
Figure 4. Exploring the necessity of fine-tuning, For 2x extrapolation, RIFLEx generates high-quality vidcos without fine-tume aiee-tuning. For 3x extrapolation, due large intrinsic frequency shift, fine-tuning required to improve dynamic effects and visual quality.
frequencies as intrinsic. Our preliminary experiments fur- ther validate this assumption, showing that incorporating all lower- lower-frequent frequency components into our method maintains strong performance, discussed in Appendix E.
Algorithm RIFLEx
Require:
The extrapolation factor 8, frequencies 0g in the RoPE, the first observed repetition frame
1: for j d' do
3.3. Reducing Intrinsic Frequency: A Minimal Solution
2: Compute the period of each 03 (Eqn. (4))
3: end for
Consider a video diffusion transformer trained on sequences of length L. We aim generate videos of length sL. via ex- trapolation by factor of s!. Based on previous findings, propose natural and minimal solution: Reducing Intrinsic Frequency for Length Extrapolation (RIFLEx). RIFLEx lowers the intrinsic frequency so that it remains within single period after extrapolation:
4: Identify the intrinsic frequency component & (Eqn. (7))
Modify 0₂ = Ls
2m
ference deviate slightly from those seen during training due to modified frequencies. While this discrepancy does not un- dermine the conclusion about our non-repetition condition in Eqn. (8), may affect visual quality since the model lacks explicit training on these specific position embeddings. Nev- ertheless, the fine-tuning process still succeeds, shown in Fig. 4.
2T
N% 2 Ls = 0 S
Ls
By setting 8% 2% we achieve minimal modification. Ablation studies on other frequencies (Appendix E) confirm that modifying only the intrinsic frequency is sufficient: adjusting nigher-frequeny components disrupt fast motion while altering lower frequencies has negligible impact. We present RIFLEx formally in Algorithm
Ls
3.4. Principled Explanation for Existing Methods
Our findings provide a principled explanation for the fail- ure patterns observed in Section 3.1. The repetition ob- served in PE and NTK (bloc97, 2023; Lu et al., 2024b) stems from their intrinsic frequency component violating the non-repetition condition in Eqn. (8). As a result, the gen- erated video content loops instead of progressing naturally, PI (Chen al., 2023b) and YaRN (Peng al., 2023) cause slow motion by interpolating high-frequency components, which are crucial for fast motion. Divided by in such meth- ods, these components cannot generate rapid movements. TASR (Zhuo et al., 2024) combines both approaches men- tioned above, resulting in mixture temporal repetition and motion slowdown. See Appendix for more details and experiments.
We further investigate whether fine-tuning is necessary for RIFLEx. Surprisingly. for 2x extrapolation, RIFLEx can generate high-quality videos in training manner, as shown in Fig. 4. Fine-tuning with only 20,000 original- length videos and 1/50,000 of the pre-training computation further enhances dynamic quality and visual quality.
For x extrapolation, the intrinsic frequency shift becomes too large, leading to notable training-teste -esting mismatch. This occurs because the position embeddings used during in-
Wee sufficiently large such that N₂ < Ls. Other- wise, it trivial to generate long videos by PE.
6

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Table Quantitative results in length extrapolation. red-marked areas the NoRepeat Score and Dynamic Degree indicate severe issues with repetition and slow motion, making other metrics meaningless, In the user study, the ratio for extrapolation represents the proportion users who prefer samples of training length over RIFLEx. The others are corresponding ranks among methods.
Automatic Metricst
User Study]
Method
NoRepeat
Dynamic
Imaging Quality
Overall Consistency
Motion
Visual Quality
Overall Aspects
Score
Degree
Quality
Cog VideoXX-X55 with 2x extrapolation, training- free
No extrapolation
67.5
64.4
25.8
66.4%
76.0%
70.2%
PE
46.6
58.6
55.0
22.9
2.1
1.6
2.4
NTK
43.4
58.3
55.3
22.9
2.1
1.8
2.1
PI
59.0
5.0
44.3
19.2
3.7
4.1
3.8
TASR
10.8
26.9
50.5
21.5
3.3
3.8
3.6
YaRN
59.4
5.6
44.6
19.3
3.6
4.2
3.7
RIFLEx (ours)
54.2
59.4
56.9
23.5
1.4
1.5
1.1
Cog CogVideoX-5B with 2x extrapolation, fine-tuning
No extrapolation
65.6
62.7
25.8
61.8%
66.0%
65.0%
PE
13.2
50.6
56.6
24.2
1.8
1.8
1.7
RIFLEx (ours)
61.3
54.7
60.4
25.0
1.2
1.2
1.3
Hunyuan Video with 2x extrapolation, training-fiee free
No extrapolation
63.0
65.9
19.6
62.8%
62.0%
61.6%
PE
36.0
63.0
64.3
19.1
2.3
1.2
2.4
NTK
81.0
55.0
65.3
18.9
1.5
1.4
1.6
PI
86.0
11.0
57.4
18.9
4.3
2.8
3,8
TASR
85.0
18.0
61.3
19.0
4.2
2.2
3.4
YaRN
86.0
15.0
58.2
18.8
3.9
2.7
3.7
RIFLEx (ours)
72.0
57.0
65.2
19.0
1.6
1.1
1.4
Hunyuan Video with 2.3x extrapolation, training free
NTK
20.0
46.0
65.5
18.3
1.7
1.6
1.7
RIFLEx (ours)
54.0
51.0
65.0
18.1
1.3
1.4
1.3
Hunyuan Video with 2x extrapolation, fine-tuning
No extrapolation
79.0
71.6
18.8
62.6%
51.2%
56.0%
PE
40.0
74.0
71.6
18.7
1.9
1.6
1.8
RIFLEx (ours)
89.0
82.0
72.0
18.1
1.1
1.4
1.2
4. Experiments
using 100 diverse prompts across multiple categories.
4.1. Setup
Evaluation metrics. Following prior work (Huang et al., 2024; Yang et al., 2024), we assess video generation using Imaging Quality, Dynamic Degree, and Subject Consistency, measuring visual quality, motion magnitude, and temporal consistency, respectively. Additionally, we introduce the NoRepeat Score, where a higher score indicates less rep- etition (detailed in Appendix D). We also conduct a user study with 10 participants, evaluating visual quality, motion quality, and overall preference. Motion quality reflects both repetition and slow motion. Users rank their preferences
We describe the dataset and evaluation setup below, with implementation details in Tab. 3 (see Appendix D).
Datasets. We use a private dataset of 20,000 videos for fine- We adopt the VBench (Huang et al., 2024) prompts to ensure consistency with prior work (Yang et al., 2024). Due to the high computational cost of Hunyuan Video (Kong al., 2024), evaluate it
tuning. For

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
PE
NTK
PI
TASR
YaRN
Ours
freee (a) Comparison of training free methods for 2x extrapolation.
NTK
Ours
(b) NTK v.s. RIFLEx for 2.3x extrapolation.
PE
Ours
(c) Comparison tne-ttunig methods extrapolation.
Figure 5. Visualization results of length extrapolation based on HlunyuanVides We achieve better video quality by effectively addressing issues slow motion and repetition. Notably, while the NTK in SanyuanVides incidentally avoids repetition extrapolation, it still encounters significant repetition longer extrapolations, such 2.3x extrapolation.
among all extrapolation methods. allowing for ties. We also perform pairwise comparisons between the results of normal samples and RIFLEx. See more details in Appendix D.
Video at 2 extrapolation, but our analysis attributes this unintended intrinsic frequency reduction that happens satisfy the non-repetition condition in Egn. (8), rather than its intended mechanism. This is evident NTK fails on Cog Video-X and Hunyuan Video with 2.3x extrapolation, reflected in its low NoRepeat Score in Tab. 1.
4.2. Performance Comparison
Results. Quantitative results summarized in Tab. 1. Our approach achieves superior overall performance, generating new temporal content without compromising other aspects of video quality. For example, in Cog VideooX-5B, PI and YaRN suffer from slow motion, leading to lower Dynamic Degree, while PE and NTK experience repetition issues, re- sulting in lower NoRepeat Score. By effectively addressing both challenges, our method significantly enhances motion quality and ranks highest in user studies across all methods.
Qualitative results are shown in Fig. 5 Hunyuan Video, with additional comparisons for CogVideoX-5B in Ap- pendix F. Fig. 5 aligns with the quantitative findings, demon- strating method's 's ability to effectively mitigate slow tion and repetition, thereby improving overall video quality.
Additionally, a minimal fine-tuning procedure requiring just 1/50,000 the pre-training computation on short videos improves the Dynamic Degree, Imaging Quality, and NoRe- peat Score. Finally, leveraging the strong Hunyuan Video base model, our approach achieves performance close
Notably, NTK coincidentally performs well for Hunyuan-
8

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RIFLEX: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
that of the training length-with 56.0% and 61.6% of users preferring the training length over our method.
(No. L247030), Beijing Nova Program (No. 20230484416), Tsinghua Institute for Guo Qiang, and the High Performance Computing Center, Tsinghua University. J. Zhu was also supported by the XPlorer Prize.
Maximum extent of extrapolation. Empirically, RIFLEx supports up to 3x extrapolation, beyond which quality de- grades significantly (e.g., at 4x extrapolation, see Fig. 9 in Appendix). This may occur because excessive frequency reduction diminishes the effectiveness of RoPE, resulting in minimal encoding changes over the training length.
Impact Statement
This paper presents work aimed advancing the field of video generation. It is crucial to use this technology respon- sibly to prevent negative social impacts, such the creation of misleading fake videos.
Extension to other extrapolation types. Video diffusion transformers typically apply ID RoPE (see Sec. 2.2) inde- pendently to both spatial and temporal dimensions. This shared mechanism leads to analogous extrapolation chal- lenges both domains. Consequently, our method naturally extends spatial extrapolation and joint temporal-spatial extrapolation, offering a unified framework for extrapolation in diffusion transformers. As shown in Fig. lb and Fig. Ic, adjusting the intrinsic frequency for the corresponding di- mensions enables resolution extrapolation and joint spatial- temporal extension. Additional demos and implementation details are provided in Appendix and Appendix D.
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5. Conclusion and Discussion
We provide comprehensive understanding of video length extrapolation by analyzing the role of frequency components in RoPE. Building on these insights, we propose RIFLEx, a minimal yet effective solution that prevents repetition by reducing intrinsic frequency. RIFLEx achieves high-quality 2x extrapolation on SOTA video diffusion transformers in training-free manner and enables 3x extrapolation by minimal fine-tuning without long videos.
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perspective. What's more, as dis- cussed the main text, the 3x limitation stems from di- minished ability to discriminate sequential positions due to excessive frequency reduction. To further extend beyond this, it is promising investigate the mechanism of po- sitional encoding during training, specifically tailored for extrapolation.
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Acknowledgements
This work was supported by NSFC Projects (Nos. 62350080, 62106122, 92248303, 92370124, 924701 18, 62350080, 62276149, U2341228, 62076147), Beijing NSF
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RIFLEX: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
A. Related Work
Length extrapolation with RoPE. Position encoding, exemplified by the widely used RoPE, plays a crucial role in enabling length extrapolation in transformers. Prior research in both language and image domains has primarily focused on training- free methods and tine-tuning under target sequence length settings. For instance, position interpolation generally outperforms direct position extrapolation in fine-tuning efficiency, requiring fewer steps adapt the target length (Chen al., 2023b), though it performs poorly in training-free settings (Zhuo et al., 2024). Advanced strategies such as NTK (bloc97, 2023) and YaRN (Peng et al., 2023) have demonstrated decent training-free performance while being more efficient in fine-tuning scenarios. Further refinements, such as optimizing RoPE frequencies (Ding et al., 2024) or modifying RoPE's extrapolation behavior (Hu et al., 2024), have shown additional improvements in language modeling. Our work provides new insights into the impact of RoPE in video diffusion transformers, introducing a length extrapolation strategy tailored for video generation. Unlike previous approaches, our proposed RIFLEx requires training only on the original pre-trained sequence length while also demonstrating strong potential in training- free settings.
Text-to-video diffusion models. Drawing upon the progress made in image generation, burgeoning body of research has emerged, focusing on the utilization of diffusion models for video generation (Kong et al., 2024; Yang et al., 2024; Ho et al., Blattmann et al., 2023; Lin et al., 2024; Xing et al., 2023; Chen et al., 2023a; 2024; He et al., 2022: Zhao et al., 2023; 2022). By combining spatial and temporal attention, VDM (He et al., 2022) introduces a space-time factorized UNet for video synthesis, marking an early contribution to the field. Later, Make-A-Vides extends the 2D-UNet with temporal modules (Singer et al., 2022), exploring the integration of prior knowledge from text-to-image diffusion models into video diffusion techniques. More recently, surge of video diffusion models leveraging the expressive power of transformers has emerged (Lin et al., 2024; Zheng et al., 2024; Kong et al., 2024; Yang et al., 2024; Bao et al., 2024; Brooks et al., 2024; Team, 2024b). These diffusion transformer- based models have demonstrated remarkable performance. Our approach builds on these advancements by applying them to pre-trained video diffusion transformers, further enhancing their capabilities. Morcover, recent developments have also seen the emergence of video diffusion models that leverage efficient attention mechanisms to accelerate their performance (Zhang et al., 2024b;a; 2025). Our approach is also applicable to these models, further extending their capabilities.
2022; Polyak et al., 2024; Brooks et al., 2024; Zhou et al., 2024; Team, 2024b; Zheng et al., 2024;
Autoregressive video generation models. Unlike diffusion models, autoregressive video generation models typically quantize videos into discrete tokens and generate video content through next-token prediction in an autoregressive manner. Previous works have demonstrated great performance in such models (Wu et al., 2021; Yan et al., 2021; Hong et al., 2022; Wu et al., 2022; Kondratyuk et al., 2023; Wu et al., 2024; Sun et al., 2024; Wang et al., 2024b). For example, NÜWA (Wu et al., 2022) employs VQ-GAN for tokenization and generates videos using a 3D transformer encoder-decoder framework. More recently, VideoPoet (Kondratyuk et al., 2023) tokenizes images and videos with a MAGVIT-v2 encoder and autoregressively generates videos using a decoder-only transformer based on a pretrained large language model. While autoregressive vidco models can theoretically extend sequences indefinitely through next-token prediction (Wang et al., 2024c; Liang et al., 2022; Ge et al., 2022), recent studies reveal their tendency to degenerate into repetitive content generation (Kondratyuk et al., 2023; Ge et al., 2022). In this work, we present principled approach to video length extrapolation that effectively generates novel temporal content in diffusion- based frameworks. Although our method is developed for video diffusion transformers, the underlying mechanism governing position embedding periodicity may also offer insights for addressing repetition challenges in autoregressive video generation.
Long video with diffusion models. Recent studies have explored long video generation with diffusion models from various angles (Lu et al., 2024a; Wang et al., 2023; 2024a;c; Lin et al., 2023; Li et al., 2024; Qiu et al., 2023; NVIDIA et al., 2025). For instance, Kim et al. (2024); Chen et al. (2025) propose diffusion sampling schemes that employ a queue of video frames with varying noise levels, progressively decoding new frames. Yan et al. (2024) introduce a cross-attention module to enhance the semantic fidelity and richness of long videos. Yin et al. (2024) distill a chunk-wise, few-step auto-regressive video diffusion transformer from a bidirectional teacher model, enabling efficient long video generation. In this work, we address long video generation with diffusion transformers through the lens of position encoding a fundamental component for capturing sequential structure in video data. We propose a minimal yet general and effective strategy that requires no training on long video data.
B. Additional Results of RIFLEx
In this section, we present additional demos for temporal extrapolation in Fig. 6, spatial extrapolation in Fig. 7, and both extrapolations in Fig. 8.
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RIFLEX: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Figure 6. More results of 2x temporal extrapolation from 129 to 261 frames.
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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
PE
Original
Original Resolution
480 x 720
480 1440 (b) length extrapolation width
Ours
Original Resolution
490 720
480 720
1440
960 x 1440
960x 720
960x 720
(a) 2x length extrapolation on both width and height
(c) 2x length extrapolation on height
Figure 7. Visualization results of spatial resolution extrapolation method in image generation. Our method outperforms the extrapolation by generating content with better visual quality.
Figure More results 2x temporal and spatial extrapolation, extending video dimensions from 480 720 x 49 to 960 1440 x 97.
Figure 9. Results of 4x temporal extrapolation from 49 to 193 frames.
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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Table
Code Links and Licenses.
Method
Link
License
Hunyuan Vidco (Kong et http://////..iiiccccccm/cmm//t// dea
Tencent Hunyuan Community License
VastVideo (Team, 2024a)
Apache License
CigexX (Yang et al., 2024)
https: pttpp:///g thub.
Apache License
Lumina- T2X (Zhuo al., 2024)
nttps://git:ii hub.. com/A -ha
MIT License
2x space extrapolation in image
length extrapolation video
Normal length
Video of 49 frames
Image 1K resolution
TASR
(a) Slower motion and temporal repetition
Super-resolution
YaRN
(b) Slower motion
(d)Blurred details
Figure Visualization other existing methods x extrapolation video image generation. YaRN leads to slower motion. While TASR can successfully perform resolution extrapolation, it simultancously causes slower motion and temporal repetition in video generation.
C. More Results of Failure Patterns of Existing Methods
As shown in Fig. 10, we present the results of other existing methods for 2x extrapolation in video and image generation. Specifically, YaRN results in slower motion, using parameters Peng et al., 2023). TASR utilizes Pl at larger timesteps and employing NTK at smaller timesteps. Consequently, it combines the characteristics of both PI and NTK, which leads to slower motion and temporal repetition in video generation.
and 3 32 as set in previous studies (Lu et al., 2024b;
D. Experimental Setup.
Used code and license. All used codes in this paper and its license are listed in Tab. 2.
Implementation details. For spatial extrapolation, following Algorithm 1, we identify the intrinsic frequency components whose periods closely match the repeating patterns observed in the height and width pixels, then adjust them to ensure unique encoding. For both spatial and temporal extrapolation, we simultaneously adjust the intrinsic frequency components for the time, width, and height dimensions. The training-free setting shares the same intrinsic frequency values as those in Tab. 3.
Evaluation metrics. For the NoRepeat Score, we identify the frame around N₁ with the minimum Le distance the first frame, marking the start of the possible repeated sequence. We then calculate the La distance between each frame the possible repeated sequence and the corresponding frame at the beginning of the video. If the average distance across frames exceeds threshold, the video has higher probability of being non-repetitive. We then calculate the proportion of videos with a higher probability of being non-repetitive. Empirically, we find that threshold of 100 aligns better with human perception, so we set it to 100. For the human evaluation the training-free setting, considering that several methods may share similar quality (c.g., slow motion with poor visual quality), we allow for ties. However, for the fine-tuning setting, ties are not permitted.
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RIFLEX: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Table Fine-tuning settings for all experiments. Both. denotes spatial and temporal extrapolation simultancously. oy. og by represent the base frequency for the intrinsic frequency in the time, height, and width dimensions, respectively. By adjusting these variables, we can modify the corresponding 0% 0g and 0g" values accordingly (refer to Section 2.2 for details).
Config
2x Temporal
2x Temporal
3x Temporal
2x Spatial
2x Both.
Base model
Cog VS
Hunyuan Video 1000
CogVideoX-SB 5000
Cog 2000
Cog CogVideox-SB
Training iterations
2500
10000
le5
560
1e6
1e5
1e6
1e6
5e4
5e4
size
180 x 720 49
960 x 129
480
720 x 19
480
720 x
x80x720x49
Batch size
8
8
8
64
8
GPU
A100-80G
24 A 1100-80G
A100-80G
A100-80G
A100-80G
Figure 11. The results of adjusting all frequency components lower than the intrinsic frequency. See detailed analysis in Appendix E.
E. Details about RIFLEx
Robustness of the intrinsic frequency k. Empirically, we collected 20 videos and found that, although the first observed repetition frame may vary across videos within a certain range, the identified intrinsic frequencies remain consistent. For example, in Hunyuan Video, even though the first observed repetition frame range from 178 200, the closest intrinsic frequency is always 4, where N₂- 200.
Adjust all frequency components lower than the intrinsic frequency. In our preliminary experiments, we slow down all frequency components lower than the intrinsic frequency by increasing the base frequency b for j 2 k. where b is chosen to satisfy the non-repetition condition Eqn. (8) for intrinsic frequency k. As shown in Fig. 11, this approach effectively addresses the repetition issue while maintaining visual quality. is important to note that, despite this choice, our RIFLEx, which reduces the intrinsic frequency, provides the minimal solution.
blations for other frequencies. As shown in Fig. 12, reducing the higher-frequeene components slows down the video. Based the analysis in Section 3.2, this may be because these components crucial for capturing fast motion. Reducing their frequencies leads to a slower rate of change in the positional encoding, which weakens the model's ability to generate rapid movements.
On the other hand, reducing the lower frequencies has a negligible impact. This is likely because, for these frequencies, the encoding functions change very little across the training length, from = 1 to p = Therefore, these frequencies may be less sensitive to positional encoding, and altering them results in minimal effect.
17

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Reference
High frequency
(a) Reducing the higher-frequency components slows down the video.
Low frequency
(b) Reducing the lower frequencies has negligible impact.
Figure 12. Ablations for reducing other frequencies. Reference refers the results of where frequencies are reduced, serving the baseline.
F. More Results about Comparisons
In this section, we show the visualization comparisons of Cog VideoX-5B. As shown in Fig. 13, and YaRN suffer from slow motion, while PE and NTK experience repetition issues. TASR suffers from both slow motion and repetition. By effectively addressing both challenges, our method significantly enhances motion quality.
18

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
NTK
Pl
TASR
YaRN
Ours
Comparison of training-free methods for extrapolation.
(b) Comparison fine-tuning methods extrapolation.
Figure 13.. Visualization results of length extrapolation based VogVideo--5B. We achieve hetter video quality by effectively addressing issues of slow motion and repetition.
19


Reviews:
[{"review_id": "bQjJjGTtTd", "reviewer": "Reviewer_EXby", "summary": "This work solves the problem of repetitiveness in long video generation from a new perspective. This work first analyzes and experiments the frequency component of the video position encoding ROPE, and concludes that the period of the frequency component directly affects the periodicity of certain characteristics of the generated video, and the frequency component closer to the video repetition period has the greatest impact. Therefore, based on this idea, this work defines an intrinsic frequency component, which has a period close to the video repetition period. Then, directly reducing this frequency component can alleviate the problem of video repetition. This method can be used in the latest and most advanced video generation models, such as CogVideoX and HuanyuanVideo, which have their corresponding intrinsic frequency components. Therefore, for video extrapolation, this method can achieve two training-free efficient fine-tuning methods:\n\n1) For training-free manner, the \"free lunch\" is to directly find the corresponding intrinsic frequency component, and then directly reduce this frequency component according to the extrapolation factor.\n\n2) For a larger extrapolation factor, a very small number of training samples can be used to fine-tune the video generation model to adapt to the reduced frequency component.\n\nThe author conducted extensive experiments on this method in CogVideoX and HuanyuanVideo, and found that it outperformed other existing video extrapolation methods in both quantitative evaluation and quality assessment. This work alleviates the problem of video extrapolation from a completely new perspective at the lowest cost, which is of great significance to the field of video generation.\n\n## update after rebuttal\n\nI would like to thank the authors for their responses in the rebuttal, which addressed my concerns. I think this is a good paper and should be accepted.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer EXby for the valuable suggestions. We have thoroughly addressed the detailed comments as follows.\n\n### Q1: Explain the proposed method is slightly worse than the best method in automatic metrics.\n\nWe kindly clarify that **only through a comprehensive consideration of multiple metrics can we objectively assess video generation quality, rather than relying solely on a single metric.** For example, while PI and YaRN achieve the highest score in the single NoRepeat Score metric, it performed significantly worse in the Dynamic Degree metric (PI/YaRN vs. our RIFLEx in Table 1), resulting in poor video quality and rendering other metrics meaningless. See Figure 5 for the evidence. To highlight this, we mark severe issues in red for clarity (see lines 330-332). We emphasize that **our method is the only one consistently highlighted in the green zone across all 5 settings in Table 1**.\n\n### Q2: The core difference between this method and others. \n\n\nOur method differs from prior works in two key aspects:\n\n- We determine **which frequency in RoPE should be modified**—specifically, the intrinsic frequency whose period aligns with the first observed repetition frame in a video (Eqn. (7)). As discussed in the main text (lines 306–311), modifying only this frequency is sufficient: adjusting higher frequencies disrupts fast motion, while altering lower frequencies has negligible impacts.\n- We derive **how to adjust this frequency**—ensuring it remains within a single period after extrapolation (the non-repetition condition, Eqn. (8)).\n\nExisting approaches modify multiple frequency components in RoPE, but **they may target incorrect components**. For instance, methods like YaRN and PI mistakenly adjust high frequencies, which results in slow motion (see lines 295–299). Furthermore, even when some methods include the correct components, **their modifications can be flawed**. For example, the adjustment of intrinsic frequency in NTK does not satisfy our non-repetition condition, resulting in repetition issues (see lines 326–329).\n\n\nIn summary, our work establishes principled guidelines for position embedding design in length extrapolation. We will make it more clear in the \"Principled Explanation for Existing Methods\" section (Section 3.4). We hope this response clearly articulates our contributions and would be very happy to clarify further concerns (if any).\n\n### Q3: Explain the gap between theory and practice and why fine-tuning can alleviate this.\n\nWe understand that the gap you are referring to is that fine-tuning the model yields better results than not fine-tuning. This arises from a training-testing mismatch, where the position embeddings used during inference slightly differ from those in training due to modified frequencies. While this discrepancy does not undermine the conclusion about our non-repetition condition, it may affect visual quality since the model lacks explicit training on these specific position embeddings. Fine-tuning helps bridge this gap by adapting the model to these variations, thereby improving visual quality. We will incorporate the above explanation of the training-testing mismatch into Section 3.3, where we discuss whether fine-tuning is a necessary.\n\n### Q4: Why does this set of ideas work in spatial extrapolation?\n\nThis is because video diffusion transformers typically apply 1D RoPE independently to both spatial and temporal dimensions (see Section 2.2, \"RoPE with Multiple Axes\"). **This shared mechanism results in similar challenges during extrapolation for both dimensions**. As illustrated in Figure 2:\n- Spatial Repetition ↔ Temporal Repetition: Both phenomena occur when intrinsic frequency components exceed a single period after extrapolation.\n- Blurred Details ↔ Slower Motion: These effects arise from interpolating high-frequency components, which are essential for spatial details in spatial domain and fast motion in the temporal domain.\n\nTherefore, our method can be extended to spatial extrapolation, providing a unified framework for extrapolation in diffusion transformers. We will add above explanation in \"Extension to other extrapolation types\" part of the final version. \n\n### Q5: Line 5 in Algorithm 1 should refer to $\\theta_k' =\\frac{2\\pi}{Ls}$.\n\nWe will correct Line 5 of Algorithm 1 to use $\\theta_k' =\\frac{2\\pi}{Ls}$ in the final version. Thank you for the careful review. \n\n### Q6: Provide the definition of the subscripts multiple times.\nAs suggested, we will explicitly define the subscript $j$ as indexing frequency components of RoPE multiple times in Section 3.1 to prevent confusion with frame indices in the final version.\n\nFinally, we sincerely appreciate the reviewer for the constructive suggestions, which help to further improve the quality of our work. We hope you may find the response satisfactory. Please let us know if you have any further feedback.", "claims_and_evidence": "- High frequencies capture rapid movements and short-term dependencies, inducing temporal repetition, while low frequencies encode long-term dependencies with slow motion. This conclusion is based on the calculation of the period of the frequency component and the repetition period of the corresponding video extrapolation. The basis here is supported by both theoretical deduction and experimental demonstration.\n- Since the intrinsic frequency component directly affects the repetition period of the video, reducing the intrinsic frequency component lengthens the repetition period of the video, so that video extrapolation can be extended to longer videos.", "methods_and_evaluation_criteria": "- This work proposes a novel \"free lunch\" for video extrapolation. This method is very simple and clear, and is a perfect solution from derivation to practical application. This method has been convincingly verified on the latest video generation model.\n- For longer video extrapolation, the method proposed in this work requires simple fine-tuning, which may be due to the gap between theoretical derivation and practical application. However, experiments show that only a small amount of fine-tuning is required, and the method can be adapted to longer video extrapolation tasks.\n- This work uses the common evaluation criteria for video generation to evaluate the results of video extrapolation, and has achieved amazing results in both quantitative evaluation and quality evaluation.", "theoretical_claims": "I checked the theoretical derivation and proof and found no obvious problems.", "experimental_designs_or_analyses": "- In the quantitative evaluation of the experimental part, in the training-free evaluation of HunyuanVideo, the proposed method is slightly worse than the best method in automatic metrics. The author can analyze the specific reasons in detail.\n- From a theoretical point of view, the biggest difference between the method proposed by the author and previous methods such as PI, NTK or YaRN is that the method in this paper focuses on the most important intrinsic frequency component rather than a group of components. Is this correct? What is the importance of this difference?", "relation_to_broader_scientific_literature": "This work alleviates the problem of video extrapolation from a completely new perspective at the lowest cost, which is of great significance to the field of video generation.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "Strengths:\n\nThe author has carried out detailed theoretical derivation and experimental verification, showing the correlation between the intrinsic frequency component and the video repetition period. This method is very simple and clear, and is a perfect solution from derivation to practical application.\n\nWeaknesses:\n- What is the core difference of this method compared with other methods? Is it to scale only the most critical frequency component, rather than a group of frequency components? The paper can discuss in detail the difference from previous methods.\n- What is the reason why the method proposed in the paper will have a gap between theory and practice? Why can fine-tuning alleviate this problem? The author can analyze this problem in detail.\n- Why does this set of ideas work in spatial extrapolation?", "other_comments_or_suggestions": "- Line 5 in Algorithm 1 should not refer to Eqn. (8), but to the equation in 306, that is, $\\theta'_{k}=\\frac{2\\pi}{Ls}$.\n- The subscripts in the formulas in the article basically refer to frequency components. The author should provide the definition of the subscripts multiple times to prevent people from misunderstanding them as frame subscripts. This is a minor concern of mine.", "questions_for_authors": "NA", "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": "BTQM1mfwdg", "reviewer": "Reviewer_JAfh", "summary": "This paper focuses on a challenging question: How to do the length extrapolation for a trained video diffusion model? After some systematic analyses, they found a metric, named intrinsic frequency that governs the extrapolation property of a video diffusion model. Then, they propose  RIFLEx to reduce the intrinsic frequency when generating longer videos. This method is valid at preserving the motion consistency and preventing the content repetition. The method enables high-quality 2× extrapolation in a completely training-free manner and supports 3× extrapolation with minimal fine-tuning on original-length videos.\n\n## update after rebuttal\nI appreciate that the authors take time to provide additional explanations. My concerns are fully addressed. I will keep my original score and support its acceptance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate Reviewer JAfh for the acknowledgement of our contributions.\n\n### Q1: Missing related work on repetition issues in autoregressive video generation models\n\n\nUnlike diffusion models, autoregressive video generation models typically quantize videos into discrete tokens and generate video content through next-token prediction in an autoregressive manner. Previous works have demonstrated great performance in such models [1–8]. For example, NÜWA [4] employs VQ-GAN for tokenization and generates videos using a 3D transformer encoder-decoder framework. More recently, VideoPoet [5] tokenizes images and videos with a MAGVIT-v2 encoder and autoregressively generates videos using a decoder-only transformer based on a pretrained large language model.\n\nWhile autoregressive video models can theoretically extend sequences indefinitely through next-token prediction [9-11], recent studies reveal their tendency to degenerate into repetitive content generation[5,11]. In this work, we present a principled approach to video length extrapolation that effectively generates novel temporal content in diffusion-based frameworks. Although our method is developed for video diffusion transformers, the underlying mechanism governing position embedding periodicity may also offer insights for addressing repetition challenges in autoregressive video generation.\n\nThank you for highlighting this important direction. We will incorporate the above discussion in related work section in the final verion. We would appreciate any additional references the reviewer could suggest that we may have missed.\n\n\n[1] GODIVA: Generating Open-Domain Videos from Natural Descriptions\n\n[2] VideoGPT: Video Generation using VQ-VAE and Transformers\n\n[3] CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers\n\n[4] NÜWA: Visual Synthesis Pre-training for Neural Visual World Creation\n\n[5] VideoPoet: A Large Language Model for Zero-Shot Video Generation\n\n[6] VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation\n\n[7] Generative Multimodal Models are In-Context Learners\n\n[8] Emu3: Next-Token Prediction is All You Need\n\n[9] Loong: Generating Minute-level Long Videos with Autoregressive Language Models\n\n[10] NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis\n\n[11] Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer\n\n\n\n### Q2: Limited exploration of extrapolation factors beyond 3×\n\nThank you for the suggestions. In this work, we primarily focus on achieving length extrapolation at minimal cost based on a pre-trained video diffusion models. As discussed in the main text, the 3× limitation stems from diminished ability to discriminate sequential positions due to excessive frequency reduction. To further extend beyond the 3× extrapolation, it is promising to investigate the mechanism of positional encoding during training, specifically tailored for extrapolation. We believe our findings colud provide valuable insights for this direction, and we will include a more detailed discussion in Section 5.\n\n\n\n### Q3: The identification method for intrinsic frequency relies on visual inspection rather than a theoretical analysis.\n\nThank you for the insightful comment. In our current work, we primarily adopt an empirical approach—visual inspection—for intrinsic frequency identification when adapting the pre-trained video diffusion transformer. While this approach is effective for adaptation, we agree that establishing a theoretical foundation for intrinsic frequency identification is crucial. Achieving this would require fundamental research into how intrinsic frequencies emerge during the pre-training process, potentially analysis from a training-from-scratch perspective. We sincerely thank the reviewer for highlighting this direction, and we will address this in our future work. We will add the above points to the discussion section.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "N/A - The paper doesn't present formal proofs for theoretical claims.", "experimental_designs_or_analyses": "Yes, the experimental designs are sound and comprehensive.", "relation_to_broader_scientific_literature": "This work effectively builds upon some previous research directions, like position embedding in diffusion model.", "essential_references_not_discussed": "Related work on repetition issues in autoregressive video generation models", "other_strengths_and_weaknesses": "Strengths: \n1. The solution is simple and effect.\n2. The overall writing is good and easy to follow.\nWeaknesses:\n1. Limited exploration of extrapolation factors beyond 3×\n2. The identification method for intrinsic frequency relies on visual inspection rather than a theoretical analysis.", "other_comments_or_suggestions": "See Other Strengths And Weaknesses", "questions_for_authors": "See Other Strengths And Weaknesses", "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": "aO4nDwok0q", "reviewer": "Reviewer_Cp7v", "summary": "This paper focused on video length extrapolation in Video Diffusion Transformers. The authors provided a comprehensive understanding of video length extrapolation by analyzing the role of frequency components in RoPE. Furthermore, a minimal yet effective method named RIFLEx is proposed to prevent repetition by reducing intrinsic frequency. Experimental results show that RIFLEx achieves high-quality 2× extrapolation on state-of-the-art video diffusion transformers in a training-free manner.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer Cp7v for the valuable comments. We address the concerns as follows.\n\n### Q1: The experimental results shown in the supplementary materials display that some cases may still suffering from temporal inconsistency and may lead to the camera to switch in the playing video. Could the authors give some explanation?\n\nWe appreciate the reviewer's attention to this detail and would like to clarify that this may be a potential misunderstanding. In fact, **multi-scene generation with camera transitions is a desirable and essential capability for video synthesis**. To achieve this, HunyuanVideo is specifically designed to curate a training dataset that includes diverse scene transitions (see Dense Description in Section 3.2 of HunyuanVideo[1]). As demonstrated on HunyuanVideo's project page[2] (e.g., the example in Row 2, Column 1), HunyuanVideo excels at \"breaking monotonous cinematography with seamless director-level shot transitions.\" This capability represents a significant advancement in video synthesis.\n\n**Our method preserves the base model's ability to generate both multi-scene videos (Videos 1-3,9 in the supplementary materials) and single-scene videos (Videos 4-8)**. Importantly, even in multi-scene generation, our method still maintains long-term temporal consistency—for instance, Figure 1 demonstrates consistent identity preservation across two distinct scene transitions. We will clarify this scene transition capability in Section 4.2 of the final version.\n\n[1] Kong, Weijie, et al. \"Hunyuanvideo: A systematic framework for large video generative models.\" arXiv preprint arXiv:2412.03603 (2024).\n\n[2] https://aivideo.hunyuan.tencent.com/.\n\n\n\n\n### Q2: Why is the effect not good when the insertion multiple is greater than 3? So how do we choose the appropriate multiples according to different models?\n\n#### Q2-1: Why is the effect not good when the insertion multiple is greater than 3? \n\nThe limitation of a 3× insertion multiple stems from a fundamental trade-off in positional encoding dynamics: an excessive reduction in frequency leads to a diminished ability to discriminate between sequential positions. Specially, a larger extrapolation factor $s$ leads to smaller frequencies $θ_k$ (see Eqn.(8)).  When $θ_k$ becomes excessively small, the position difference term $∆ = \\cos((p + 1)θ_k) − \\cos(p\\theta_k)$ diminishes to near-zero values, thereby losing positional discriminability. Empirically, we find the threshold occurs at $θ_k′ \\le 2\\pi/3L$ when $s=3$. Furthermore, **this upper limit is inherent to the positional encoding mechanism and is consistent across existing pretrained video diffusion models** based on our experiments. We will add the above detailed explanation in \"Maximum extent of extrapolation\" part of the final version. \n\n#### Q2-2: So how do we choose the appropriate multiples according to different models?\n\nWe clarify that **we do not select different $s$ for different models**. Instead, the extrapolation length is determined by the user's requirements, provided it remains below the upper limit. For example, in the paper, we demonstrate results for generating videos with multipliers of $2$, $2.3$, and $3$. Based on the analysis in Q2-1, the extrapolation limit is consistent across current models, eliminating the need for specific designs tailored to different models.\n\nWe hope you might find the response satisfactory, and we would be delighted to clarify any further concerns you might have.", "claims_and_evidence": "This paper claims that \"generating even longer videos with temporal coherence remains a major challenge and existing length extrapolation methods lead to temporal repetition or motion deceleration.\" and validates this by experimental results.", "methods_and_evaluation_criteria": "A minimal yet effective method named RIFLEx is proposed to prevent repetition by reducing intrinsic frequency. Qualitative and quantitative evaluations are conducted.", "theoretical_claims": "No theoretical claims", "experimental_designs_or_analyses": "Yes, a minimal yet effective method named RIFLEx is proposed to prevent repetition by reducing intrinsic frequency.", "relation_to_broader_scientific_literature": "This paper focused on video length extrapolation in Video Diffusion Transformers. The authors provided a comprehensive understanding of video length extrapolation by analyzing the role of frequency components in RoPE. Furthermore, a minimal yet effective method named RIFLEx is proposed to prevent repetition by reducing intrinsic frequency. Experimental results show that RIFLEx achieves high-quality 2× extrapolation on state-of-the-art video diffusion transformers in a training-free manner.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Pros:\n\n1.\tA minimal yet effective method named RIFLEx is proposed to prevent repetition by reducing intrinsic frequency.\n2.\tThis paper is well written and easy to follow.\n3.\tExperimental results verify the effectiveness of the proposed method.\n\nCons:\n\n1.\tThe experimental results shown in the supplementary materials display that some cases may still suffering from temporal inconsistency and may lead to the camera to switch in the playing video. Could the authors give some explanation?\n2.\tWhy is the effect not good when the insertion multiple is greater than 3? So how do we choose the appropriate multiples according to different models?", "other_comments_or_suggestions": "Please refer to Weaknesses for more details", "questions_for_authors": "Please refer to Weaknesses for more details", "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": "FI3a2M4keL", "reviewer": "Reviewer_DWAu", "summary": "This paper propose a training-free method for video extrapolation. It argues that existing extrapolation strategies, originally developed for text and image generation, fail on videos because of temporal repetition and slow motion. It analyzes the frequency components in positional encoding, isolating individual frequency components by zeroing out others and fine-tuning the target video model. It finds that high frequencies capture short-term dependencies and induce temporal repetition, while low frequencies encode long-term dependencies but lead to motion deceleration. Furthermore, this paper identifies a consistent intrinsic frequency component across different videos from the same model, which primarily  dictates repetition patterns among all components during extrapolation. Based on the observation, this paper proposes to lowers the intrinsic frequency to ensure it remains within a single cycle after extrapolation. In addition, this technique can also be applied to spatial extrapolation.\n\nExperiments are on state-of-the-art video diffusion transformers, including CogVideoX-5B and HunyuanVideo for 2x extrapolation. Besides the training-free method, the paper also explores the possibility of fine-tuning, which improves the sample quality and extends to 3x extrapolation.\n\n## update after rebuttal\nThe rebuttal has addressed my concerns and questions. Since I already gave accept, I will keep the score.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer DWAu for the recognition of our work. The further questions are addressed as follows.\n\n### Q1: If only 20,000 videos are needed, how to select them? Does the selection make any difference?\n\nThe 20K videos in this paper were randomly sampled without selection. Fine-tuning aims to adapt the model to modified frequencies, which theoretically requires no data bias. To validate this, we add an experiment where we independently train models on two distinct randomly sampled datasets. We then perform three sampling runs with different random seeds and apply a two-sample $t$-test to compare the performance of the two models. As demonstrated in Rebuttal Table A, the statistical analysis reveals no significant performance difference between the models at the $95$% confidence level (all two-sample $t$-test $p$-values > $0.05$, $α=0.05$).\n\n**Rebuttal Table A.** Performance comparison between Model A and Model B, trained on two independent randomly sampled datasets based on the CogVideoX-5B architecture. Evaluation is conducted on a 165-sample subset of VBench with three sampling runs, reporting the mean ± standard deviation. The p-values are derived from a two-sample $t$-test.\n| Metric| Model A / Model B| P Value|\n|--|--|--|\n|NoRepeat Score| 81.21 $\\pm$ 7.572 / 81.82 $\\pm$ 6.414 | 0.9212|\n|Dynamic Degree| 53.24 $\\pm$ 3.493 / 62.03 $\\pm$ 7.650 | 0.1444|\n|Imaging Quality| 60.30 $\\pm$ 0.8279 / 58.96 $\\pm$ 0.8874 | 0.1270|\n|Overall Consistency| 25.26 $\\pm$ 0.2974 / 25.21 $\\pm$ 0.2053| 0.7994|", "claims_and_evidence": "1. Claim: A comprehensive understanding of video length extrapolation.\n\n  Evidence: Qualitative results show that the positional encoding based previous extrapolation methods gives either repeated frames or slow motion. Quantitative results show that there exists a consistent intrinsic frequency component across different videos from the same model.\n\n2. Claim: A training-free extrapolation solution by reducing the intrinsic frequency.\n\n  Evidence: Qualitative results show that reducing the intrinsic frequency indeed help in video extrapolation.\n\n3. Claim: 2x extrapolation in the training-free manner and 3x extrapolation in the fine-tuning manner.\n\n  Evidence: Qualitativer results shows fine-tuning is necessary for 3x extrapolation. The fine-tunning takes 20,000 original length videos and 1/50,000 of the pre-training computation.", "methods_and_evaluation_criteria": "The method is simple yet effective. By only reducing the intrinsic frequency for those video generation models, the extrapolation length is significantly longer. The effectiveness is demonstrated quantitatively by NoRepeat Score and Dynamic Degree, and qualitatively by the supplementary videos.", "theoretical_claims": "I checked the frequency analysis in RoPE, which makes sense.", "experimental_designs_or_analyses": "The experiments look sound.", "relation_to_broader_scientific_literature": "In a broader sense, I believe this pre-context aware video extrapolation is also related to video prediction. The key of both areas is how to generate the next frames with the context of existing frames.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "N/A", "questions_for_authors": "If only 20,000 videos are needed, how to select them? Does the selection make any difference?", "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:

Meta-review: All 4 reviewers recommended acceptance (1 weak accept, 3 accepts). After reading the paper and reviews, I agree with the reviewers and recommend acceptance.
Decision: Accept (poster)