FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention
Authors: Yu Lu
Yuanzhi Liang
Linchao Zhu
Yi Yang
Venue: NeurIPS 2024 poster (2024)
Keywords: text-to-video generation; diffusion models; long video generation
Primary area: generative_models

Abstract: Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computational and data resources, posing a challenge to developing long video diffusion models.
This paper investigates a straightforward and training-free approach to extend an existing short video diffusion model (e.g. pre-trained on 16-frame videos) for consistent long video generation (e.g. 128 frames).  Our preliminary observation has found that directly applying the short video diffusion model to generate long videos can lead to severe video quality degradation. Further investigation reveals that this degradation is primarily due to the distortion of high-frequency components in long videos, characterized by a decrease in spatial high-frequency components and an increase in temporal high-frequency components. Motivated by this, we propose a novel solution named FreeLong to balance the frequency distribution of long video features during the denoising process. FreeLong blends the low-frequency components of global video features, which encapsulate the entire video sequence, with the high-frequency components of local video features that focus on shorter subsequences of frames. This approach maintains global consistency while incorporating diverse and high-quality spatiotemporal details from local videos, enhancing both the consistency and fidelity of long video generation. We evaluated FreeLong on multiple base video diffusion models and observed significant improvements. Additionally, our method supports coherent multi-prompt generation, ensuring both visual coherence and seamless transitions between scenes. Our project page is at: https://yulu.net.cn/freelong.

FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention
Yu Lut, Yuanzhi Liang?, Linchao Zhu', Yi Yang!*
'Te State Key Lab of Brain-Machine Intelligence, Zhejiang University University of Technology Sydney
Abstract
Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computational and data resources, posing a challenge developing long video diffusion models. This paper investigates straightforward and training-free approach extend existing short video diffusion model (e.g.. pre-trained on 16-frame videos) for consistent long video generation (e.g.. 128 frames). Our preliminary observation has found that directly applying the short video diffusion model to generate long videos can lead to severe video quality degradation. Further investigation reveals that this degradation is primarily due to the distortion high-frequency components in long videos, characterized by decrease in spatial high-frequency components and an increase in temporal high- high-frequency compo- nents. Motivated by this, propose novel solution named FreeLong to balance the frequency distribution of long video features during the denoising process. FreeLong blends the low-frequency components of global video features, which encapsulate the entire video sequence, with the high-frequency components local video features that focus shorter subsequences of frames. This approach maintains global consistency while incorporating diverse and high-quality spa- tiotemporal details from local vidcos, enhancing both the consistency and fidelity of long video generation. We evaluated Lone on multiple base video diffusion method supports
models and observed significant improvements. Additionally, generation, ensuring both visual coherence and seamless transitions between scenes. Our project page is at: http:://yllannetcn/ffeeeoog
coherent
Introduction
Video diffusion models 222
45 6 7 trained on vast video-text datasets 88 9 have demonstrated
impressive capabilities generating high-quality videos. Inspired by Sora [10, multiple studies [1]
12
13
have concentrated on training these models to create longer videos using extensive, long
video-text datasets 114
15
16
17
[8]. However, these methods demand significant computational
resources and data annotations.
more practical approach involves adapting pre-trained short video models generate consistent longer video sequences without retraining. Recent research [19 20 has explored sliding window temporal attention to ensure smooth transitions between video clips in the generation of long videos. Nonetheless, these techniques often struggle to maintain global temporal consistency across extended sequences and require multiple passes of temporal attention.
In this study, we propose a simple, training-free method to adapt existing short video diffusion models (e.g., pretrained on 16 frames) for generating consistent long videos (e.g., 128 frames). Initially,
CCoresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024)

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tiger walks in the furest, photorealistic, 4G, high definition
a polar bear playing drum kir in NYC Times Square,
LaViie Irannes
LaVie 16 frames
128
128
L.Ve 125 Trasiiss
LaVie 126 frunies
ErecLong 126 frannss
FroeLong 128 Teamnes
A girl walked along a forest path, survounded by call teee
A Corgi walking in the park at sunrise, oil painting style
NidenCraffer
NidunCraflie
Viduon Crafter frames
Videorrafirr 128 Brances
128
128
Freekong 128 fruntes
FreeLong 126 frames
Figure I: Results of Short and Long Videos. The first row of each case shows 16-frame videos
generated using short video diffusion models (LaVie M and VideoCrafter2 22.. Directly extending these models to longer videos, like those with 128 frames, preserves temporal consistency but lacks fine spatial-tempora details. In contrast, our proposed FreeLong adapts short video diffusion models to create consistent long videos with high fidelity.
we examine the direct application of short video diffusion models for long video generation. As depicted in Figure []] straightforwardly using 16-frame video diffusion model to produce 28-frrmme sequences yields globally consistent yet low-quality results.
To delve further into these issues, we conducted frequency analysis of the generated long videos. As shown in Figure (a). the low-frequency components remain stable as the video length increases, while the high-frequency components exhibit noticeable decline, leading to a drop in video quality. The findings indicate that although the overall temporal structure is preserved, fine- aine-grained details suffer notably in longer sequences. Specifically, there is a decrease in figh-frequency spatial components (Figure 2 (b)) and an increase in fighffreeueccy temporal components (Figure 2 (c)). This figh-freuuncc distortion poses a challenge in maintaining high fidelity over extended sequences. As illustrated in the middle row of each case in Figure T intricate textures like forest paths or sunrises become blurred and less defined, while temporal flickering and sudden changes disrupt the video's narrative flow.
To tackle these challenges, we introduce Freel.ong, a novel framework that employs SpectralBlend Temporal Attention (SpectralBlend-TAN to balance the frequency distribution of long video features in the denoising process. SpectralBlend- TA integrates global and local features via two parallel streams, enhancing the fidelity and consistency long video generation. The global stream deals with the entire video sequence, capturing extensive dependencies and themes for narrative continu- ity. Meanwhile, the local stream focuses on shorter frame subsequences to retain fine details and smooth transitions, preserving gighffreuenncy spatial and temporal information. SpectralBiend- combines global and local video features the frequency domain, improving both consistency and fidelity by blending low-frequency global components with high-frequency local components. Our method is entirely training-free and allows for the easy integration of Freel.ong into existing video diffusion models by adjusting the original temporal attention of video diffusion models. Comparative

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experiments demonstrate significant improvements in temporal consistency and video fidelity when applying our method generate long video sequences.
Our contributions can be summarized as follows: 1) We conduct a frequency analysis on the direct application of short video models for longer video generation and identify high-frequency distortions in the longer videos. 2) We devise SpectralBlend Temporal Attention mechanism to merge the consistent low-frequency components of global videos with the high-fidelity high- frequency components of local videos. 3) Our training-free approach, FreeLong, outperforms existing stateoof-the-ar models in both temporal consistency and video fidelity.
2 Related Work
Text-to- Video Diffusion Models: Text-to-video (T2V) generation has progressed significantly from
early variational autoencoders (21 and GANs 23 to advanced diffusion-based techniques (3]
4 24 25 26), marking major leap in synthesis methods. Modern video diffusion models build
on pre-trained image-to-text diffusion models (27] 28 (2). incorporating temporal transformers in the diffusion UNet to capture temporal relationships. These models achieve impressive video generation results through post-training on video-text data [14 9 g 1. enhancing coherence and fidelity. However, due to computational constraints and limited dataset availability, current video diffusion models are typically trained on fixed-length short videos (e.g.. 16 frames), limiting their ability to produce longer videos. In this paper, we propose extending these short video diffusion models to generate long and consistent videos without requiring any additional training videos.
Long-video Generation: Generating long videos is challenging due to temporal complexity, resource constraints, and the need for content consistency. Recent advancements focus on improving temporal coherence and visual quality using GAN-based [30 31 and diffusion-basee techniques [32 33 34
35| (6).
For instance, Nuwa-XL (36) employs parallel diffusion process, while Streaming autoregressive approach with a short-long memory block to improve the consistency of long video sequences. Despite their effectiveness, these methods require substantial computational resources and large-scale datasets. Recent research has explored training-free free adaptations using short video diffusion models for long video generation. Gen-L- [37] extends videos by merging overlapping sub-segments with sliding-window method during denoising. FreeNoise [19] employs sliding-wing wiiddw temporal attention and a noise initialization strategy to maintain temporal consistency, However, these approaches focus smooth transitions between video clips and fail capture global consistency across long video sequences. This paper proposes FreeLong, novel approach that blends global and local video features during the denoising process enhance both global temporal consistency and visual quality in long video generation.
3 Observation and Analysis
When attempting to adapt short video diffusion models to generate long videos, a straightforward approach is to input longer noise sequence into the short video models. The temporal transformer layers in the video diffusion model are not constrained by input length, making this method seemingly viable. However, Generated long videos often exhibit fewer detailed textures, such as blurred forests the background, and more irregular variations, like abrupt changes in motion. We attribute these issues to two main factors: the limitations of the temporal attention mechanism and the distortion of high-frequency components.
empirical study reveals significant challenges,
demonstrated in Figure
Attention Mechanism Analysis: The temporal attention mechanism in vidco diffusion models is pre-trained on fixed-length vidcos, which complicates its ability to generate longer videos. As shown in Figure 3. increasing video length hinders the temporal attention's is ability to accurately capture frame-to- frame relationships. For 16- 16-frame videos, the attention maps show diagonal pattern, indicating high correlations with adjacent frames that preserve spatial-temporal details and motion patterns. In contrast, for 128 128-fram frame videos, the less structured attention maps suggest difficulty in focusing on relevant information across distant frames, leading to missed subtle motion patterns and over-smoothed or blurred generations.
Frequency Analysis: To better understand the generation process of long videos, we analyzed the frequency components in vidcos of varying lengths using the Signal-to-Noise Ratio (SNR)
3

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Moshods
Fiweaag lilsk
Dbst lllgs
1.4
SNB (40)
0.90
SNK (00)
0.9
16 truse 8011
SSit (a0)
Mettoods
Ponteng
Fonlang
0.8
Methods
Dirxt High
0.75
Dirsk Lov
Gurer S201
liiga
fram
Enume
Snume
Frumn
Erume
Vidvo Length
Video Length
Video Length
Spaco-Time SNR
Time
Figure 2: Ratio of short video SNR high/low frequency to different long videos. Our findings reveal that: (a) When direct extend short video diffusion model to generate long videos, the SNR of high-frequency components in the space-time frequency domain degrades significantly as video length increases. (b) In the spatial frequency domain, the SNR of high-frequency components decreases even more substantially, resulting in the smoohhing of each frame. (c) Conversely, in the temporal frequency domain, the SNR of high- feeh-freuuency components increases significantly, introducing temporal flickering.
LaVie
VideoCrafter
16 Frame
64 Frame
128 Frame
16 Frame
64 Frame
128 Frame
Figure 3: Temporal Attention Visualization. We visualize the temporal attention by average across all layers and time steps from LaVie [1 and VideoCrafter (2). The attention maps for 16-frame videos exhibit diagonal-like pattern, indicating high correlation with adjacent frames, which helps preserve high- freeefrey details and motion patterns when generating new frames. In contrast, attention maps for longer videos are less structured, such as 128 frames, making the model struggle to identify and attend to the relevant information across distant frames. This lack of structure in the attention maps results in the distortion of high-frequee frequency components of long videos, which results in the degradation of fine spatial-tempon details.
metric. Ideally, short video diffusion models generate 16-frame videos with high quality, and robust longer videos derived from such models should exhibit consistent SNR values across all frequency components, However, Figure 2 reveals significant differences in the SNR of high/low frequency componenty" between generated short and long videos. The SNR of low-frequency components remains relatively consistent for long videos (1.0 for 16 frames to 0.93 for 128 frames), suggesting that the model maintains overall structure and low-frequency details in extended sequences. However, the SNR of high-frequency components drops significantly for longer videos (1.0 for 16 frames to 0.73 for 128 frames), indicating a loss fine details and increased distortion, leading to suboptimal visual fidelity.
Further investigation into the spatial and temporal frequency domains revealed two key findings: (I) In the spatial domain, the high-frequency components of long videos degrade significantly (0.68 68 for 128 frames), causing substantial degradation of spatial details in each frame and resulting in blurred frames. (2) In the temporal domain, the high-frequeng components increase with video length (1.5 for 28-frame videos), resulting temporal flickering and incoherent video outputs.
We split the frequency components into high figh-freueecy (ø (0.25% 1.00m) )) and low-frequency
(Ø (0. (0.00T - 0.25m)) and compared the SNR of each component in long videos to the corresponding SNR in 16 66-frmme videos.

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Iterative Denoising
Attention Blocks
Space alto
x T
Decoder
Cross alln
SpetmiBkand-AA
ZT
Zo
3D FFT
Local-TA
High Pass
Local
Local
F.l Zglnbal
Z'
Global-12 TA
3D FPT
Local-global
ntoobal
gglobal
Spectral Blending
Attention Decoupling
Figure 4: Overview of FreeL.ngg. FreeLong facilitates consistent and high-fidelity video generation using SpectralBlend Temporal Attention (SpectralBlend-TAA) SpectralBlend-TA effectively blends low-frequency global video features with high-frrquency local video features through two-step process: local-global attention decoupling and spectral blending. Local video features are obtained by masking temporal attention to concentrate attention encompasses all frames. During spectral blending, 3D FFT projects features into the frequency domain, where high-free frehffrygyency local components and low-frequency global components are merged. The resulting blended feature, transformed back to the time domain via IFFT, is then utilized in the subsequent block for refined video generation.
fixed-length adjacent frames, while global temporal
FreeLong: Training-free Long Video Generation
Motivated by the above analysis, we propose FreeLongg method designed to generate high-fidelity and consistent long videos using the inherent power of the diffusion model. As illustrated Figure { our FreeLong uses diffusion UNet from pre-trained short video diffusion models and introduces a SpectralBlend Temporal Attention (SpectralBlend-TA. to facilitate long video generation. The SpectralBlend-TA consists of two steps: local-global attention decoupling and spectral blending.
Local-global Attention Decoupling:
The temporal attention in short video models is optimized to model short frame sequences accurately, maintaining high-fidelity visual information. Conversely, the long-range temporal attention from short video models tends to maintain overall layout and and object consistency. Given these properties, we first decouple the local and global attention. The local attention matrix can be obtained as:
Q.K,
Softmax
if i 5
Atocul (i,
Vd
(1)
otherwise,
where Q and K are the query and key matrices derived from the input video feature Z The local attention Alocal leads to each frame only attending to frames within window the local attention matrix Alecl the local video features Ziocal be obtained by: Ziocal Atocal V. where is the value matrix derived from the input video feature Zyn By restricting the temporal attention to adjacent local frames, we preserve the capabilities of short video models, thereby retaining high-fidelity visual details in local video features.
frames. Given
We then define the global attention matrix where each frame attends to all other frames. The global attention matrix can be computed follows:
QUKJ]
Aglobal (i.j) Softmax
Vd
Given the global attention matrix Aglohal, the global video features Zglobal can be obtained by: Zelobal = Agiohal V. The global video features process the entire video sequence, ensuring narrative continuity and coherence, while capturing long-range dependencies and overarching themes.
5

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After obtaining the global and local video features, frequency filter is used blend the low-frequency components of the global video latent Z globat with the high- tigh-freuueccy components of the local video latent Ziocat, resulting in a new video latent Z'. This fused latent retains the global coherence and structure provided by Zittbbat while benefiting from the enhanced high-frequency details introduced by Zocatt The process is described by:
Spectral Blending:
FL FFT 3D Z globbal)
① P,
(3)
Sylobul
FHl 3FT Zrocal ) 2 - P);
(4)
Stocal
Z' = 1DFT gglobal + F
(5)
where FFT is the Fast Fourier Transformation operated on both spatial and temporal dimensions, IFFT is the Inverse Fast Fourier Transformation that maps back the blended representation Z' from the frequency domain, and P E R 1x xh is the spatial-temporal Low Pass Filter (LPF), which is tensor of the same shape as the latent. The final fused video feature Z' serves as the input to our subsequent video generation module.
The rationale behind using low-frequency components from the global video features and high- frequency components from the local video features stems from our analysis. The global features provide stable, coherent structure, preserving the overall layout and object consistency throughout the video. This is crucial for maintaining temporal consistency in long videos. On the other hand, local features retain high-fidelity details, which are essential for capturing fine textures and intricate motion patterns that tend to degrade in long sequences. By blending these components in the frequency domain, harness the strengths of both global consistency and local detail preservation, addressing the issues of blurred frames and temporal flickering observed in our analysis.
indicate that latent diffusion models (27) generate varying levels of visual content at different stages of the denoising process: scene layout and object shapes in the early steps, and fine details in the later steps. We propose fusing global and local video features in the early steps of the denoising process and using local video features in the remaining steps. This fusion ensures that the overall layout and object appearance of the generated long video follow the global features, thereby maintaining temporal consistency in the generated videos.
Recent studies [38 39]
5 Experiments
5.1 Implementation Details
Baseline Models: To evaluate the effectiveness and generalization of our proposed method, apply FreeLong on two publicly available diffusion-based text-to-video models, LaVie [ and VideoCrafier (2). These models are trained short videos with fixed length (i.e., 16 frames), extend them to produce long videos (i.e., 128 frames [40). We set a = 8 for the local attention setting and set to 25. During inference, the parameters of the frequency filter for each model are kept the same for a fair comparison. Specifically, we use a Gaussian Low Pass Filter (GLPF) Pg with normalized spatiotemporal stop frequency of D 0.25. Multi-prompy videos are generated with random noise, and FreeNoise [1 is used for single-prompt long video generation.
Test Prompts: We chose 200 prompts from VBench 41 validate the effectiveness of our method.
For text-to-video generation, we employed several metrics from VBench [41] to evaluate two aspects: video consistency and video fidelity. For video consistency measurement, we use metries: 1). Subject consistency, computed by the DINO A2 feature similarity across frames to assess whether object appearance remains consistent throughout the whole video. 2). Background consistency, calculated by CLIP (43 feature similarity across frames. For video fidelity measurement, we use three metrics: 1). Motion smoothness, which utilizes the motion priors in the video frame interpolation model AMT 44 to evaluate the smoothness of generated motions. 2). Temporal flickering. which takes static frames and computes the mean absolute difference across frames. 3). Imaging quality, calculated using the MUSIQ 45 image quality predictor trained on the SPAQ [46] dataset.
Evaluation Metrics:

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Table 1: Quantitative Comparison. "Direct sampling" and "Sliding window" indicate directly sampling 128 frames and applying temporal sliding windows based on short video generation models, respectively. Compared to these methods, our FreeLong achieves consistent long video generation with high fidelity.
Methods
Sub (t)
Back (f)
Motion (1)
Flicker (t)
Imaging
Inference Time (4)
Direct sampling
88.95
93.23
92.77
91.44
64.76
1.8s
Sliding window
85.80
92.83
95.79
94.00
66.57
2.6s
FreeNoise [9]
92.30
95.87
96.32
94.94
67.14
2.68
Ours
95.16
96.80
96.85
96.04
67.55
2.2s
Results from LaVie
Direct
Sliding-
Sliding-
PreeNaise
Freel ong
Prompt: A travoling and paosing bridge.
Prompt: sining fire pit
Results from VideoCrafter
Dirrct
Dirvel
Sldding Window
Siding Window
FreeNoise
FreeNeise
FroeL.og
FreeLong
Procutt: Truck carrying the tree logs.
Promuit: mon euting donut.
Qualitative Comparison. Results from LaVie 1 and VideoCrafter 2 presented. Direct videos exhibit consistent frames, but they appear over-smoothed. FreeNoise and the sliding- window approach struggle capture global consistency effectively. Our FreeLong method achieves consistent long video generation while maintaining high fidelity, preserving crucial details and textures across the entire sequence.
Figure 5:
5.2 Quantitative Comparison
We compare our FreeLong method with other training- freee approaches for long video generation using diffusion models. Our comparison includes three methods: (1) Direct sampling. directly samples 128 frames from the short video models. (2) Sliding window. It adopts temporal sliding
windows 20 to process fixed number frames at time. (3) FreeNoise [99] FreeNoise introduces repeat input noise maintain temporal coherence across long sequences.
Table presents quantitative results. Direct generation of long videos suffers from high-frequency distortion, leading to significant quality degradation. This method results in low fidelity scores, including imaging quality, temporal flicering, and motion smoothness. The sliding-window method and FreeNoise show improved video quality thanks to the fixed effective temporal attention window

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Global
Local
Global-L.ocm
Low Frequency of Global
High Frequency
FreeLong
Figure 6: Ablation Study. Global features and low-frequency components of global features ensure consistency but degrade fidelity. Local features and high-frequency local features maintain spatial-temporal details but lack temporal consistency. Directly adding global and local features degrades fidelity. Our method achieves both high fidelity and temporal consistency.
but still face challenges in maintaining consistency across long videos. Our FreeLong method achieves the highest scores across all metrics, producing consistent long videos with high fidelity. Moreover, we also examine the inference time of these methods on the NVIDIA A100. As delineated in Table [ our approach achieves faster speed compared to preceding methods by employing single-pass temporal attentions.
5.3 Qualitative Comparison
The synthesis results of each method are shown Figure In the first row, directly sampling 128 frames through a model trained 16 frames will bring poor quality results due the high-frequency distortion. For example, the yacht (left) and the girl (right) have blurred and the background is clear. As shown in second row in Figure 5 using temporal sliding windows helps generate more vivid videos, but this approach ignores long- maaggrange visual consistency, causing the subject and background to appear significantly different across frames. FreeNoise attempts to promote global consistency by repeating and shuffling the initial noise for each frame: however, it fails to maintain long-range visual consistency and suffers from content mutations. In contrast, our method, FreeLong, explicitly enforces global constraints during the denoising process, achieving temporal consistency while preserving high fidelity across frames. Results shown in Figure demonstraate that Freel.ong successfully renders temporal consistent longer videos, outperforming all other methods.

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Prompi:; I: Ironnaa running on the road, 4K, high resulation
Prompt2: Tronman standing on the road, 4K, high resolution
Prompt3: fronman flying on the sky, 4K, high
Prompt1: The morning sun rises, illuminating solitary lighthouse rocky shore.
Prompi2: Clouds gather, and rain fall, the lighthouse stand firm under storm with heavy and thunder.
Prompt3; The storm clears, the lighthouse stand under rainbow.
Prompi4: Night falls, and the lighthouse raise light through the darkness.
Promptl: A musician with guitar performs the edge of mountain morning:
Prompi2: The
performs busy urban park, crowd gathers, enchanted music.; Promp13: A serene lakeside sunsel where the musician plays alone, reflecting.
Prompt:: Night falls, and the musician joins a lively street festival, lights and music filling the air;
Prompt4; The journey ends on a quiet balcony overlooking the city at night, the musician performs towards the city.
Figure Results of Multi-Prompt Video Generation. Our method ensures coherent visual continuity and motion consistency across different video segments.
5.4 Ablation Studies
To validate the effectiveness of each module in our FreeLong method-globar globbll video feature, local video feature, and our combined approach-we present the generated results by ablating each component.
As shown in the top part of Figure 6 videos generated solely from global video features maintain consistent content but suffer from severe fidelity degradation. Conversely, videos generated using only local video features preserve fidelity due the fixed effective temporal attention window but fail to maintain temporal consistency, as evidenced by the changing color of the cow. Simply combining global and local video features results in fidelity degradation because the high-frequency components of the global video features degrade significantly.
In the bottom part of Figure components from global video features with the high-frequency components from local video features. Our approach effectively combines the consistency of global videos with the high fidelity of local videos, achieving both high fidelity and temporal consistency.
show the videos generated by combining the low-frequency
5.5 Multi-Prompt Video Generation
Our method be seamlessly extended multi-prompt video generation by providing different prompts for each video segment. As illustrated in Figure 7 our approach ensures coherent visual continuity and motion consistency. For instance, Ironman shown running on the road, then standing, and finally flying into the sky, all within consistent scene and with smooth action transitions. In the second row, we demonstrate more complex prompt sequence describing weather and scene transitions. Our method effectively models the transition from "sunrise" to "storm with heavy rain and thunder" to the final "rainbow," maintaining consistency and capturing the fine-neained details of each prompt transition.
5.6 Longer Video Generation
To examine the scalability of our Freel.ong, we extend the video generation length beyond 128 frames. As depicted in Figure 8 FreeLong efffectively generates videos with even longer durations, such as

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128
256
128
256
256 Fraines
25 franes
128
FreeLang 25% Tranes
Freelang 256 frames
197
364
197
197
364
256
512
Freel 512 fraimes
Figure 8: Longer Video Generation. FreeLong scales generate videos longer than 128 frames (e.g., 512 frames), maintaining both temporal consistency and high fidelity across the entire sequence.
512 frames, while maintaining both temporal consistency and high fidelity throughout the entire sequence. This demonstrates that our method scales well with increasing video lengths, addressing the challenges associated with generating long continuous content without significant degradation in quality.
Conclusion
In this paper, introduced FreeLongg training-free method to adapt short video diffusion models for long video generation. Our research reveals that directly generating long videos from short video diffusion models results in poor quality, primarily due high-frequency distortion. To resolve this issue, we employ the SpectralBlend Temporal Attention (SpectralBlend-TA5, mechanism, which blends low-frequency global features with high-frequency local features to enhance consistency and fidelity in long videos. Our experiments demonstrate that FreeLong significantly outperforms existing models, achieving superior temporal consistency and video fidelity. Our experiments show that FreeLong significantly outperforms existing models, achieving better temporal consistency and video fidelity. FreeLLng also supports coherent multi-pro pollippprompt generation, offering a practical solution for high-quality long video creation without extensive retraining.
7
Acknowledgments
This work supported by National Science and Technology Major Project (2022ZD01 17802) and the National Natural Science Foundation of China (U2336212). This work is partially supported by the Fundamental Research Funds for the Central Universities (Grant Number: 226-2024-00058).
10

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References
[1]
Yaohui Wang, Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang. Yinan He, Jiashuo Yu, Peiqing Yang, et al. Lavie: High- high-quality video generation with cascaded 2023.
latent diffusion models. arXiv preprint arXiv:2309.1511
[2] Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overeoming data limitations for high-quality video diffusion models, 2024.
[3] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXi::2205. 2022.
[4] Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai. Ani- matediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307. 04425, 2023.
[5] Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang. Modelscope text-to-video technical report. arXiv preprint arXiv:2308. 06571, 2023.
[6] Yu Lu, Linehao Zhu, Hehe Fan, and Yi Yang. Flowzero: Zero-shot text-to- video synthesis with Ilm-driven dynamic scene syntax. arXiv preprint arXiv:2311.15881 2023.
[7] Xiangpeng Yang, Linchao Zhu, Hehe Fan, and Yi Yang. Eva: Zero-shot accurate attributes and multi-object video editing. arXiv preprint arXiv:2403.1 1611,, 2024.
[8] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Internvid: large-scale video-text dataset for multi- modal understanding and generation. In The Twelfth International Conference on Learning Representations, 2023.
[9] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In IEEE International Conference on Computer Vision, 2021.
[10] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng. Ricky Wang, and Aditya Ramesh. Video generation models as world simulators, 2024. Accessed: 2024-05-09.
[11] Roberto Henschel, Levon Khachatryan, Daniil Hayrapetyan, Hayk Poghosyan, Vahram Tade- vosyan, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Streamingt2v: Consistent, dynamic, and extendable long video generation from text. arXiv preprint arXiv:2403. 14773, 2024.
[12] Fan Bao, Chendong Xiang, Gang Yue, Guande He, Hongzhou Zhu, Kaiwen Zheng, Min Zhao, Shilong Liu, Yaole Wang, and Jun Zhu. Vidu: a highly consistent, dynamic and skilled text-to-video generator with diffusion models. arXiv preprint arXiv:2405.04233, 2024.
[13] Ye Tian, Ling Yang, Haotian Yang, Yuan Gao, Yufan Deng, Jingmin Chen, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, et al. Videotetris: Towards compositional text-to-video generation. arXiv preprint arXiv:2406.00427) 2024.
[14] Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wei Chao, Byung Fun Jeon, Yuwei Fang, Hsin-Ying Lee, Jian Ren, Ming-Hsuan Yang. and Sergey Tulyakov. Panda-70m: Captioning 70m videos with multiple cross-modality teachers. arXiv preprint arXiv:2402.19477 2024.
[15] Wenjing Wang, Huan Yang, Zixi Tuo, Huiguo He, Junchen Zhu, Jianlong Fu, and Jiaying Liu. Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation. arXiv preprint arXiv:2305. 2023.
[16] Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, and Yali Wang. Vlogger: Make your dream vlog, arXiv preprint arX1v:2401.09414, 2024.
[17] Fuchen Long, Zhaofan Qiu, Ting Yao, and Tao Mei. Videodrafter: Content-consistent multi-
scene video generation with Ilm. arXiv preprint arXiv:2401.0/2555 2024.
[18] Yu Lu, Feiyue Ni, Haofan Wang, Xiaofeng Guo, Linchao Zhu, Zongxin Yang, Ruihua Song, Lele Cheng, and Yi Yang. Show me video: A large-scale narrated video dataset for coherent story illustration. IEEE Transactions Multinedia, 2023.
11

---

[19] Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He, Xintao Wang, Ying Shan, and Ziwei Liu. Freenoise: Tuning-free -ineg-free longer video diffusion via noise rescheduling. arXiv preprint arXiv:2310. 1519,, 2023.
[20] Fu-Yun Wang. Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, and Hongsheng Li. Gen-I-video: Multi-text to long video generation via temporal co-denoising. arXiv preprint arXiv:2305. 2023.
[21] Yitong Li, Martin Renqiang Min, Dinghan Shen, David E. Carlson, and Lawrence Carin. Video generation from text. CoRR, abs/1710.00421, 2017.
[22] Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David E. Carlson, and Jianfeng Gao. Storygan: A sequential conditional GAN for story visualization. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 6329-6338 Computer Vision Foundation / IEEE, 2019.
[23] lan J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial networks. CoRR, abs/ Abs/1406.2261,, 2014.
[24] Daquan Zhou, Weimin Wang, Hanshu Yan, Weiwei Lv, Yizhe Zhu, and Jiashi Feng. Magicvideo: Efficient video generation with latent diffusion models. CoRR, abs/2211.1 1018, 2022.
[25] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. Make-a-video: Text-to-video generation without text-video data. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. Open 2023.
[26] Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, and Ziwei Liu. Freeinit: Bridging initialization gap in video diffusion models. arXiv preprint arXiv:2312.0753) 2023.
[27] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022.
[28] Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang. Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22511-22521, 2023.
[29] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, et al. Photorealistic coxt-to-image diffusion models with deep language understanding, Advances in neural information processing systems, 35:36479-36494, 2022.
[30] Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elhoseiny. Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 3626-3636, 2022.
[31] Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming- Yu Liu, Alexei Efros, and Tero Karras. Generating long videos of dynamic scenes. Advances in Neural Information Processing Systems,
2022.
[32] William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, and Frank Wood. Flexible diffusion modeling of long videos. Advances in Neural Information Processing Systems, 35:27953-27965. 2022.
[33] Vikram Voleti, Alexia Jolicoeur- Martineau, and Chris Pal. Mevd-masked conditional video dif- fusion for prediction, generation, and interpolation. Advances in neural information processing systems, 35:23371-23388, 2022.
[34] Hung- Yu Tseng, Qinbo Li, Changil Kim, Suhib Alsisan, Jia- Bin Huang, and Johannes Kopf. Consistent view synthesis with pose-guided diffusion models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 16773-16783, 2023.
[35] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising difffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
[36] Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, et al. Nuwa-xl: Diffusion over diffusion for extremely long video generation. arXiv preprint arXiv:2303. 2023.
12

---

[37] Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Ye, Yu Liu, and Hongsheng Li. Gen-I-video: Multi-text to long video generation via temporal co-denoising, arXiv preprint arXiv:2305. 18264, 2023.
[38] Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, al. ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
[39] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22560- 22570, 2023.
[40] Chengxuan Li, Di Huang, Zeyu Lu, Yang Xiao, Qingqi Pei, and Lei Bai. A survey on long video generation: Challenges, methods, and prospects. arXiv preprint arXiv:2403. 2024.
[41] Ziqi Huang, Yinan He, Jiashuo Yu. Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. arXiv preprint arXiv:2311.1798. 2023.
[42] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660, 2021.
[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pages 1748-8765 PMLR, 2021.
[44] Zhen Li, Zuo-Liang Zhu, Ling- Han, Qibin Hou, Chun-Le Guo, and Ming- Cheng. Amt:
All-pairs multi-field transforms for efficient frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9801-9810 100, 2023.
[45] Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1157-115, 2021.
[46] Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang. Perceptual quality assessment of smartphone photography. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3677-3686, 2020.
[47] Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj. and Thomas Wolf. Diffusers: State-of-the-art diffusion models. hhtpps
//github.comm www..oggiggonacaeeeeeeeooooo sefffaffuddfffeeesseeeeess 2022.
[48] Zangwei Zheng. Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li. Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all, March 2024.
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A Appendix
A.1 Social Impacts
It is important to consider the potential ethical implications of our approach, which is typical in generative models. By incorporating Video Diffusion Model 2 into our methodology, there is chance that our system may also inherit the biases present in these models. Additionally, we need be aware of the potential risks, including the generation of deceptive, harmful, discriminatory content.
A.2
Limitation
Despite its significant advancements, FreeLong has several limitations. Temporal flickering can still occur in extended sequences, affecting the visual consistency over prolonged videos. Additionally, handling dynamic scene changes where context and content vary significantly remains challenging. as the current model may struggle to adapt to rapidly changing scenarios. Nonetheless, FreeLong rep- resents promising approach training-free free long-for text-to-video generation, offering significant improvements in consistency and fidelity despite these challenges.
A.3
Code used and License
All used codes and their licenses are listed in Table [
Table 2: The used codes and license.
URL
Ciation
License
kttps: // /ithbb. con/ /ehi tect/LViie
Apache Lioense 2.0
attps: //gbbbbb..bb....../..../.h..bb.. com/ hww.ghugaceeeeeaeaeeeeeee
Apache
kEtps:/ thtbb....th....../.../........ con Lab CVC/ ViC/Vaeaarratta
Apache License 2.0
Attpa://g
ttub, /ttp:///gthu....om
cont mode http://///ooeeooo///
Apache
A.4 More Qualitative Results
We add more video generation results in Figure 9
14

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120
camel roaming in the field during daytime
40
70
100
bonfire river
10
40
70
100
120
close up video of a dragonfly
120
Beun
happy kid playing the ukulele
10
40
70
100
120
male police officer talking on the radio
100
120
person paddling wooden boat in lake
10
40
70
video of yacht sailing in the ocean
10
120
woman sitting the indoor fire pit
70
100
120
close up on lobster under
Figure More Long Video Generation Results.
15

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An estronaut floaring in space, high quality, reselition
astronunt walking on the moon's surface,, high-quality
Medehhagpe 16 Trante
128
FreeLang frances
face, body
playing draw NYC
Asirestediff
OpentSon
Animatedilf 138 frames
Openbera EP6 francs
64
12H
FroeLing
ProdLang 11A EcHEticS
Figure 10: Generalization to Other Base Models. Freel.ong can be easily integrated into various video diffusion frameworks by replacing the temporal attention with SpectralBlend-TA, enabling these models to generate consistent long videos with high fidelity.
A.5
Generalization to Other Base Models
Our FreeLong method designed be model-agnostic and can seamlessly integrated into various pre-trained short video diffusion models. To validate this, apply FreeLong other state-of-the-an art video diffusion frameworks beyond LaVie [ and VideoCrafter [2]. Specifically, we incorporate FreeLong into models like ModelScope [5], Animatediff [4] and OpenSora 48]] replacing their original temporal attention mechanisms with our proposed SpectralBlend Temporal Attention. As illustrated in Figure TO FreeLong successfully enhances these models' capabilities to generate consistent long videos with high fidelity. The generated videos maintain temporal coherence across extended sequences while preserving fine-grained spatial-temporal details. The successful integration and performance improvement across different models highlight the generalizability of our approach. This fiexibility allows researchers and practitioners extend the capabilities of various short video diffusion models without additional training.
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Reviews:
[{"review_id": "di4h31dr3C", "reviewer": "Reviewer_838f", "summary": "The paper presents FreeLong, a novel training-free method for generating extended videos (128 frames) using pre-trained short video (16 frames) diffusion models. The key component is the SpectralBlend Temporal Attention (SB-TA), which fuses low-frequency global video features with high-frequency local features to ensure consistency and fidelity in long video generation. Experimental results highlight FreeLong's superior performance in video fidelity and temporal consistency over existing methods.", "questions": "Some concerns please refer to the Weaknesses. \nAlso what about the longer duration of video generation, for example, videos longer than 128 frames?", "limitations": "Including examples of failure cases would provide a better understanding of the method's limitations.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Comprehensive Analysis**: The paper conducts an in-depth frequency analysis to identify challenges in long video generation and supports the proposed solution with extensive experimental validation.\n2. **Novel SB-TA Mechanism**: The proposed SpectralBlend Temporal Attention effectively mitigates high-frequency component degradation, ensuring consistent and high-fidelity video outputs.\n3. **Training-Free Long Video Generation**: Long video generation is an important task and future direction. I am happy to see a training-free method appear in the community. The proposed FreeLong is both resource-efficient and practical, allowing for the adaptation of existing short video models to long video generation without the need for retraining.\n4. **Multi-Prompt Generation Support**: The multi-prompt video generation is coherent within FreeLong, and the visual continuity and smooth transitions between different scenes are satisfactory.", "weaknesses": "1. Inference time comparisons should include both single-pass and multi-pass temporal attention from previous methods to demonstrate the advantages clearly.\n2. Although the video generation results are impressive, a more extensive user study is required to validate the consistency and quality of the generated videos.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **Inference time comparisons**\n> \n\nThank you for your question. Following your advice, we conducted a comparison of inference times. As shown in the table below, our method achieves faster inference speeds than multi-pass methods, such as FreeNoise.\n\n| Method | Inference Time |\n| --- | --- |\n| Direct | 1.8s |\n| Sliding Window | 2.6s |\n| FreeNoise | 2.6s |\n| FreeLong(Ours) | 2.2s |\n\n> **User study**\n> \n\nThank you for your valuable suggestions. We conducted a user study to evaluate temporal consistency, fidelity, and overall rankings. Ten participants from academia and industry took part in this study. The results are as follows:\n\n| Method | Consistency $\\uparrow$ | Fidelity $\\uparrow$ | Overall rank $\\downarrow$ |\n| --- | --- | --- | --- |\n| Direct | 2.47 | 1.85 | 3.96 |\n| Sliding Window | 2.03 | 2.30 | 3.45 |\n| FreeNoise | 2.36 | 2.81 | 1.48 |\n| FreeLong(Ours) | 3.14 | 3.04 | 1.11 |\n\nOur method, FreeLong, outperformed other methods across all evaluated criteria.\n\n> **Longer Videos**\n> \n\nWe have provided longer videos, including results with 256, 384, and 512 frames, in the one-page PDF. These results demonstrate the generalization ability of FreeLong to handle longer videos.", "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": "qf9e9G2cgh", "reviewer": "Reviewer_oAuU", "summary": "The auther propose FreeLong, a training-free method for long video generation. This paper identifies that the problem with long video generation lies in the scope of attention and the distortion of high-frequency components. Based on the observation, the auther proposes a novel method to blend different frequency components of global and local video, leading to better result in long video generation.", "questions": "1) Computing the global attention map and video features seems computationally expensive, what is the computational cost compared to previous method (like freenoise) ?\n2) Method like freenoise suffer from repetitive generation. Generated contents will repeat several times. Can freelong solve this problem?", "limitations": "Yes", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The research problem is important. The paper is well-written and easy to follow.\n2. The observation and the method are reasonable.\n3. Experiments results look promising.", "weaknesses": "My main concern lies in the computational cost.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **what is the computational cost compared to previous method (like freenoise) ?**\n> \n\nBased on your feedback, we conducted a comparison of inference times between our method, FreeLong, and other methods, including FreeNoise, the sliding window method, and direct application. The results are summarized in the table below:\n\n| Method | Inference Time |\n| --- | --- |\n| Direct | 1.8s |\n| Sliding Window | 2.6s |\n| FreeNoise | 2.6s |\n| FreeLong(Ours) | 2.2s |\n\nOur method, FreeLong, utilizes both global and local attention streams, performing a single-pass temporal attention forward operation. In contrast, previous methods like FreeNoise rely on sliding window temporal attentions, which require multiple rounds of temporal attention and thus increase inference time. Our results demonstrate that FreeLong achieves faster inference time.\n\n> **Can freelong solve the repetitive generation problem?**\n> \n\nRegarding the repetitive generation problem observed in method FreeNoise, it is primarily due to their reliance on repetitive noise initialization. Our proposed method, FreeLong, overcomes this issue by explicitly capturing global consistency during the temporal attention process. This enables FreeLong to generate consistent longer videos without depending on repetitive initial noises.\n\nBy effectively capturing global consistency, FreeLong produces more diverse and coherent video sequences, which significantly reduces the repetitive generation problem. Figure 3 in the submitted one-page PDF provides a comparison of the results between FreeLong and FreeNoise, demonstrating our approach's effectiveness in reducing repetitive generation while maintaining high-quality video generation results.", "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": "se9u8rg92b", "reviewer": "Reviewer_XZSx", "summary": "This paper proposes a training-free method to generate 8x longer videos based on 16-frame pre-trained video diffusion models. It observes that extended temporal attention has a negative effect on high-frequency generation. Thus, it proposes an SB-TA module to fuse global low-frequency temporal features and local high-frequency temporal features.", "questions": "1. In Lines 33-38 and Fig. 1, the authors claim that longer video generation on short-clip pre-trained models will degrade high-frequency details. However, the results of FreeLong still do not have rich details (especially in the background).\n\n2. Did you consider the memory cost of long-video generation with one forward pass (128 frames)?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Lines 42-43 & 107-121: The observation on SNR with spatial-temporal components is natural and reasonable.\n\n2. Novelty: While frequency splitting and merging are common in low-level image processing tasks like super-resolution, this is the first solution for video temporal \"super-resolution\".", "weaknesses": "1. Lines 69-76: The related work section is not reader-friendly for those unfamiliar with the field. Simply listing papers across a wide range (from GAN to diffusion) is too vague to introduce the outline of the research field.\n\n2. Lack of comparisons and baselines: The observation analysis and experiments only include two base models: LaVie and VideoCrafter. Note that both of these video diffusion models adopt relative positional encoding (PE) in their temporal attention blocks. Other models like AnimateDiff (absolute PE), DIT-based models like Open-Sora (RoPE), and ModelScope (which adds temporal convolution, no PE) are not discussed in the paper. This suggests that the proposed method may be limited to one specific PE.\n\n3. The visual result quality is limited and does not include more motions, despite the frame rate being 8x (16 to 128). Although the authors show multi-prompt generation ability in Sec. 5.5, a single prompt should still be enough to cover a much longer motion. (e.g., Fig.7's first prompt, \"running Iron Man,\" is a complex motion, and it should be generated correctly as a complete motion with 128 frames sampling. Can you show this case in the rebuttal?)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback. We have carefully considered your comments and suggestions to improve our paper. Below are our responses to each of your concerns:\n\n> **The related work section is not reader-friendly for those unfamiliar with the field**\n> \n\nWe apologize for the lack of clarity in the related work section. We understand that it may be challenging for readers unfamiliar with the field. To address this, we will revise this section to provide a more comprehensive overview of the research landscape. We will include a structured summary that highlights the key developments and contributions in video diffusion models, making it more accessible and informative for all readers.\n\n> **Lack of comparisons and baselines**\n> \n\nWe appreciate your insightful feedback regarding the need for more comparisons and baselines. Initially, we chose LaVie and VideoCrafter2 due to their state-of-the-art performance in video generation. However, we recognize the importance of demonstrating the generalizability of our proposed method, FreeLong, across different base models.\n\nIn response, we have extended our experiments to include additional models, such as OpenSORA, AnimateDiff, and ModelScope. Figure 2 in the one-page pdf presents these results, showing that FreeLong performs well across these diverse models. This broader evaluation demonstrates that FreeLong is not limited to a specific positional encoding and can be effectively applied to various video diffusion models.\n\n> **The visual result quality is limited and does not include more motions**\n> \n\nWe understand your concern about the visual result quality and the limited motion content in our generated videos. For single-prompt inputs, we focus on maintaining consistency a cross long sequences, while for multi-prompt inputs, we aim to facilitate more complex and accurate motions.\n\nAs illustrated in Figure 3 of the one-page pdf, our method achieves more natural variations compared to FreeNoise. FreeNoise often generates repetitive content due to repeated noise initialization to maintain temporal consistency. In contrast, FreeLong utilizes global temporal attention to explicitly maintain temporal consistency without sacrificing motion variety.\n\n> **FreeLong still do not have rich details**\n> \n\nThank you for pointing out the need for richer high-frequency details, particularly in the background. FreeLong aims to improve both consistency and fidelity in long video generation. To validate spatial details of generated videos, we used the MUSIQ model from VBench to evaluate image quality~(e.g. over-exposure, noise, blur). The results are shown in the table below:\n\n| Method | 16 frame |  Direct | Sliding Window | FreeNoise | Ours |\n| --- | --- | --- | --- | --- | --- |\n| Imaging Quality | 0.6890 | 0.6298 | 0.6591 | 0.6645 | 0.6913 |\n\nOur method achieves comparable image quality to 16-frame videos, demonstrating improved spatial detail. More qualitative examples on longer videos and additional base models are provided in Figure 1,2 in the one-page pdf.\n\n> **Memory Cost**\n> \n\nWe have calculated the computational cost, including inference time and memory usage, as shown in the table below:\n\n| Method | Inference Time | Memory Cost |\n| --- | --- | --- |\n| Direct | 1.8s | 18251MiB |\n| Sliding Window | 2.6s | 15017MiB |\n| FreeNoise | 2.6s | 15017MiB |\n| FreeLong(Ours) | 2.2s | 20179MiB |\n\nCompared to the direct application method, our FreeLong method slightly increases both memory cost and inference time to achieve consistent and high-fidelity long video generation. FreeLong can generate 128-frame videos using a single NVIDIA RTX 4090 GPU, ensuring feasibility for practical use.\nWhen compared to FreeNoise, FreeLong reduces the inference time per step from 2.6s to 2.2s while increasing memory usage. This highlights FreeLong's efficiency in generating longer videos, as it effectively balances inference time with memory cost.\nBy optimizing these trade-offs, FreeLong demonstrates its capability to generate longer videos efficiently.", "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": "YvOqvK7jVc", "reviewer": "Reviewer_QZ6p", "summary": "The paper introduces a SpectralBlend Temporal Attention (SB-TA) mechanism, which blends low-frequency and high-frequency components from attention features together to enhance consistency and realism in generating long videos. The authors tested the proposed algorithm using 25 text prompts on LaVie and VideoCrafter, demonstrating improvements in VBench metrics. Additionally, they conducted ablation studies to showcase the characteristics of different modules in the proposed method.", "questions": "1. Figure 2's right panel demonstrates strong diagonal correlations in VideoCrafter at 64-frame and 128-frame intervals. How does this correlate with the findings proposed by the authors?\n2. Since the focus of this paper is to test the algorithm's ability in long video generation, while VBench primarily benchmarks the quality of short video generation, the key question is how to demonstrate that the results obtained reflect the algorithm's performance in long video generation quality.", "limitations": "The method proposed by the authors can only generate videos up to 128 frames, which falls short of true long video generation. It has yet to be validated in generating genuinely long videos.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed method achieved notable improvements, and the authors conducted interesting analyses to support their findings.\n2. The proposed method supports multi-prompt long video generation.", "weaknesses": "1. My first concern is that 128 frames itself cannot be considered long video generation. Therefore, it would be more convincing to see how effective the authors' proposed method is on longer videos, such as generating 1-minute videos like Sora.\n2. The proposed method includes several hyperparameters, such as alpha and tau. Are these parameters significant in influencing the final results? Because the authors tested only 25 prompts, it's unclear whether these hyperparameters remain suitable across a broader range of scenarios.\n3. Although adding local features to long-range features can mitigate some loss of spatial details,  for longer video generation with significant content variation, it remains uncertain whether these local features adequately supplement spatial details.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **My first concern is that 128 frames itself cannot be considered long video generation. Therefore, it would be more convincing to see how effective the authors' proposed method is on longer videos, such as generating 1-minute videos like Sora.**\n> \n\nThank you for your feedback regarding the length of the generated videos. Following other works such as [1,2,3], which consider videos longer than 10 seconds as \"long,\" we chose 128 frames to speed up our experiments. We understand that 128 frames may not fully represent long video generation compared to examples like Sora. It is important to note that our FreeLong method is not limited to this length. To address your concern, we have conducted further experiments evaluating FreeLong on generating longer videos of 256, 384, and 512 frames (approximately 1 minute at 8 fps). These results, shown in Figure 1 of the submitted one-page PDF, indicate that FreeLong achieves robust longer video generation across these lengths.\n\n[1] StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text\n\n[2] A Survey on Long Video Generation: Challenges, Methods, and Prospects\n\n[3] FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling\n\n> **The proposed method includes several hyperparameters, such as alpha and tau. Are these parameters significant in influencing the final results?**\n> \n\nWe appreciate your question regarding the significance of hyperparameters such as $\\alpha$ and $\\tau$. We set $\\alpha$ to match the pre-trained video length. To explore the impact of $\\tau$, we conducted quantitative ablation studies, as shown in the table below:\n\n| $\\tau$ | sub consistency | back consistency | motion_smooth | imaging_quality |\n| --- | --- | --- | --- | --- |\n| 10 | 0.9236 | 0.9323 | 0.9685 | 0.6830 |\n| 20 | 0.9350 | 0.9358 | 0.9709 | 0.6893 |\n| 30 | 0.9462 | 0.9578 | 0.9758 | 0.6894 |\n| 40 | 0.9667 | 0.9660 | 0.9754 | 0.6913 |\n| 50 | 0.9601 | 0.9645 | 0.9736 | 0.6941 |\n\nBigger $\\tau$ means more steps to blend global and local features, the table shows that our method keeps enhancing the consistency when the steps increase and saturated at step 40.\n\n> **Because the authors tested only 25 prompts, it's unclear whether these hyperparameters remain suitable across a broader range of scenarios.**\n> \n\nRegarding the number of test prompts, as described in Lines 178-179, we randomly selected 25 prompts from each of the 8 test categories in VBench, resulting in a total of 200 prompts to ensure a fair comparison. This approach ensures a fair and comprehensive comparison across all categories. All test prompts are illustrated in the supplementary materials.\n\n> **Although adding local features to long-range features can mitigate some loss of spatial details, for longer video generation with significant content variation, it remains uncertain whether these local features adequately supplement spatial details.**\n> \n\nThank you for pointing out the concern regarding the adequacy of spatial detail supplementation. We use the MUSIQ model, which evaluates over-exposure, noise, and blur of image, to validate image quality of the generated longer videos. The results are as follows:\n\n| Method | 16 frame | Direct | Sliding Window | FreeNoise | Ours |\n| --- | --- | --- | --- | --- | --- |\n| Imaging Quality | 0.6890 | 0.6298 | 0.6591 | 0.6645 | 0.6913 |\n\nOur method achieves comparable image quality to 16-frame videos, and significantly outperforms other methods. More qualitative examples on longer videos and additional base models are provided in Figure 1,2 in the one-page pdf.\n\n> **Figure 2's right panel demonstrates strong diagonal correlations in VideoCrafter at 64-frame and 128-frame intervals. How does this correlate with the findings proposed by the authors?**\n> \n\nThank you for your valuable question. VideoCrafter2 is pre-trained on varied video lengths and is relatively robust to longer video generation than LaVie. To better understand the temporal relationships in temporal attention, we set the diagonal elements, such as (0,0), (1,1) to 0, which makes visualization of relationships between different frames more effective. As shown in Figure 4 of one-page pdf, the attention map reveals less structured attention patterns. This approach helps explain the long-range temporal attention of VideoCrafter2.\n\n> **how to demonstrate that the results obtained reflect the algorithm's performance in long video generation quality.**\n> \n\nThank you for your valuable suggestions. VBench is a comprehensive evaluation benchmark for video diffusion models, with evaluation tools not constrained by video length, such as CLIP similarity, DINO similarity, and Temporal Flickering. Since no long video benchmark was available before the paper submission, we used this benchmark. Per your suggestion, we compared our method on the VBench-Long [4], designed for Sora-like video evaluation, as shown in the table below. It is evident that our method achieves consistent performance improvement compared to other methods.\n\n| Method | sub consistency | back consistency | motion_smooth | imaging_quality |\n| --- | --- | --- | --- | --- |\n| Direct | 0.8687 | 0.9151 | 0.9229 | 0.6298 |\n| Sliding Window | 0.8811 | 0.9089 | 0.9574 | 0.6591 |\n| FreeNoise | 0.9397 | 0.9511 | 0.9696 | 0.6645 |\n| FreeLong(Ours) | 0.9667 | 0.9660 | 0.9754 | 0.6913 |\n\n[4] https://github.com/Vchitect/VBench/tree/master/vbench2_beta_long", "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:
We thank all reviewers for engaging in the review process. Our code will be made public upon acceptance.

We are deeply encouraged by positive comments from the reviewers. We appreciate the recognition and endorsement of our proposed training-free pipeline, such as acknowledging its analysis and method as reasonable (**838f,oAuU**), interesting (**QZ6p**), novel (**XZSx**), and effective (**838f,**). **838f**, **oAuU**, and **QZ6p** agree that our method generates videos that achieved notable improvements.

In our individual replies, we attempted to address specific questions and comments as clearly and in detail as possible.

Moreover, we added several additional results to the one-page PDF and the individual replies to strengthen our work overall. Here, we briefly summarize these additional experiments and evaluations:

- Longer videos from 256 to 512 frames
- Generalization to other base models with different position encoding, including ModelScope, Animatediff, and OpenSORA
- Detail comparison to FreeNoise
- More clear attention visualization on VideoCrafter2

We hope that these additional results further strengthen FreeLong position as the state-of-the-art method of training-free extending short video diffusion models to generate longer sequences and demonstrate:

- Our FreeLong can effectively generate longer video sequences not limited by fixed 128 frames.
- Our FreeLong is robust and is compatible to different video diffusion models and enhances the long context consistency and fidelity.

Meta-review: This paper received four positive ratings after the rebuttal. The reviewers are satisfied with the rebuttal since it addressed the unclear issues in method design and experimental performance. The AC has read the paper, reviews, and rebuttal, making the decision of acceptance based on the consensual opinion of the reviewers. However, the authors still need to address the remaining issues regarding the approach effectiveness as suggested by the reviewers.
Decision: Accept (poster)