FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention

Authors: yu  lu
yuanzhi  liang
linchao  zhu
yi  yang
Venue: NeurIPS_cc_2024_Conference
Published: 2024-04-23 13:36:44

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.

Main paper: 
 introduction: 
 Video diffusion models [1, 2, 3, 4, 5, 6, 7] trained on vast video-text datasets [8, 9] have demonstrated impressive capabilities in generating high-quality videos. Inspired by Sora [10] , multiple studies [11, 12, 13] have concentrated on training these models to create longer videos using extensive, long video-text datasets [14, 15, 16, 17, 18] . However, these methods demand significant computational resources and data annotations.A more practical approach involves adapting pre-trained short video models to 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, The first row of each case shows 16-frame videos generated using short video diffusion models (LaVie [1] and VideoCrafter2 [2] ). Directly extending these models to longer videos, like those with 128 frames, preserves temporal consistency but lacks fine spatial-temporal 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 1 , straightforwardly using a 16-frame video diffusion model to produce 128-frame sequences yields globally consistent yet low-quality results.To delve further into these issues, we conducted a frequency analysis of the generated long videos. As shown in Figure 2 (a), the low-frequency components remain stable as the video length increases, while the high-frequency components exhibit a noticeable decline, leading to a drop in video quality.The findings indicate that although the overall temporal structure is preserved, fine-grained details suffer notably in longer sequences. Specifically, there is a decrease in high-frequency spatial components (Figure 2 (b) ) and an increase in high-frequency temporal components (Figure 2 (c) ). This high-frequency distortion poses a challenge in maintaining high fidelity over extended sequences.As illustrated in the middle row of each case in Figure 1 , 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 FreeLong, a novel framework that employs SpectralBlend Temporal Attention (SpectralBlend-TA) 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 of long video generation. The global stream deals with the entire video sequence, capturing extensive dependencies and themes for narrative continuity. Meanwhile, the local stream focuses on shorter frame subsequences to retain fine details and smooth transitions, preserving high-frequency spatial and temporal information. SpectralBlend-TA combines global and local video features in 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 FreeLong into existing video diffusion models by adjusting the original temporal attention of video diffusion models. Comparative experiments demonstrate significant improvements in temporal consistency and video fidelity when applying our method to 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 a SpectralBlend Temporal Attention mechanism to merge the consistent low-frequency components of global videos with the high-fidelity highfrequency components of local videos. 3) Our training-free approach, FreeLong, outperforms existing state-of-the-art models in both temporal consistency and video fidelity. 
related work: 
 Text-to-Video Diffusion Models: Text-to-video (T2V) generation has progressed significantly from early variational autoencoders [21, 22] and GANs [23] to advanced diffusion-based techniques [3, 4, 24, 25, 26] , marking a major leap in synthesis methods. Modern video diffusion models build on pre-trained image-to-text diffusion models [27, 28, 29] , 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, 8, 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-based techniques [32, 33, 34, 35, 36] . For instance, Nuwa-XL [36] employs a parallel diffusion process, while StreamingT2V [11] uses an 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 adaptations using short video diffusion models for long video generation. Gen-L-Video [37] extends videos by merging overlapping sub-segments with a sliding-window method during denoising. FreeNoise [19] employs sliding-window temporal attention and a noise initialization strategy to maintain temporal consistency. However, these approaches focus on smooth transitions between video clips and fail to capture global consistency across long video sequences. This paper proposes FreeLong, a novel approach that blends global and local video features during the denoising process to enhance both global temporal consistency and visual quality in long video generation.Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: In the appendix, we discussed our limitations, societal impact, and directions for future work. Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
observation and analysis: 
 When attempting to adapt short video diffusion models to generate long videos, a straightforward approach is to input a 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, our empirical study reveals significant challenges, as demonstrated in Figure 1 . Generated long videos often exhibit fewer detailed textures, such as blurred forests in 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.Attention Mechanism Analysis: The temporal attention mechanism in video diffusion models is pre-trained on fixed-length videos, which complicates its ability to generate longer videos. As shown in Figure 3 , increasing video length hinders the temporal attention's ability to accurately capture frame-to-frame relationships. For 16-frame videos, the attention maps show a diagonal pattern, indicating high correlations with adjacent frames that preserve spatial-temporal details and motion patterns. In contrast, for 128-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 videos of varying lengths using the Signal-to-Noise Ratio (SNR) as a 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 a diagonal-like pattern, indicating a high correlation with adjacent frames, which helps preserve high-frequency 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-frequency components of long videos, which results in the degradation of fine spatial-temporal 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 components2 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 of fine details and increased distortion, leading to suboptimal visual fidelity.Further investigation into the spatial and temporal frequency domains revealed two key findings: (1) In the spatial domain, the high-frequency components of long videos degrade significantly (0.68 for 128 frames), causing substantial degradation of spatial details in each frame and resulting in blurred frames. (2) In the temporal domain, the high-frequency components increase with video length (1.5 for 128-frame videos), resulting in temporal flickering and incoherent video outputs. 𝑍 "#&%"ℱ % !"#$%" & ℱ % "#&%" ' Local-global Attention Decoupling 𝑍 !"#$%"𝑍 "#&%"Space attnSpetralBlend-TA Attention BlocksQuestion: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [NA] • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 
freelong: training-free long video generation: 
 Motivated by the above analysis, we propose FreeLong, a method designed to generate high-fidelity and consistent long videos using the inherent power of the diffusion model. As illustrated in Figure 4 , our FreeLong uses a 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.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:EQUATIONwhere Q and K are the query and key matrices derived from the input video feature Z in . The local attention A local leads to each frame i only attending to frames within a window of 2α frames. Given the local attention matrix A local , the local video features Z local can be obtained by:Z local = A local V ,where V is the value matrix derived from the input video feature Z in . 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.We then define the global attention matrix where each frame attends to all other frames. The global attention matrix can be computed as follows:EQUATIONGiven the global attention matrix A global , the global video features Z global can be obtained by: Z global = A global V .EQUATIONEQUATIONEQUATIONwhere FFT 3D is the Fast Fourier Transformation operated on both spatial and temporal dimensions, IFFT 3D is the Inverse Fast Fourier Transformation that maps back the blended representation Z ′ from the frequency domain, and P ∈ R 4×N ×h×w is the spatial-temporal Low Pass Filter (LPF), which is a 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 highfrequency components from the local video features stems from our analysis. The global features provide a 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, we harness the strengths of both global consistency and local detail preservation, addressing the issues of blurred frames and temporal flickering observed in our analysis.Recent studies [38, 39] 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 τ 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. 
implementation details: 
 Baseline Models: To evaluate the effectiveness and generalization of our proposed method, we apply FreeLong on two publicly available diffusion-based text-to-video models, LaVie [1] and VideoCrafter [2] . These models are trained on short videos with fixed length (i.e., 16 frames), we extend them to produce long videos (i.e., 128 frames [40] ). We set α = 8 for the local attention setting and set τ 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) P G with a normalized spatiotemporal stop frequency of D 0 = 0.25. Multi-prompt videos are generated with random noise, and FreeNoise [19] is used for single-prompt long video generation.Test Prompts: We chose 200 prompts from VBench [41] to validate the effectiveness of our method.Evaluation Metrics: 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 two metrics: 1). Subject consistency, computed by the DINO [42] 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. Prompt: A man eating a donut. Prompt: Truck carrying the tree logs. [1] and VideoCrafter [2] are presented. Direct videos exhibit consistent frames, but they appear over-smoothed. FreeNoise and the slidingwindow approach struggle to 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.We compare our FreeLong method with other training-free approaches for long video generation using diffusion models. Our comparison includes three methods: (1) Direct sampling. It directly samples 128 frames from the short video models. (2) Sliding window. It adopts temporal sliding windows [20] to process a fixed number of frames at a time. (3) FreeNoise [19] . FreeNoise introduces repeat input noise to maintain temporal coherence across long sequences.Table 1 presents the 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 flickering, and motion smoothness. The sliding-window method and FreeNoise show improved video quality thanks to the fixed effective temporal attention window 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 1 , our approach achieves a faster speed compared to preceding methods by employing single-pass temporal attentions.The synthesis results of each method are shown in Figure 5 . In the first row, directly sampling 128 frames through a model trained on 16 frames will bring poor quality results due to the high-frequency distortion. For example, the yacht (left) and the girl (right) have blurred and the background is not clear. As shown in second row in Figure 5 , using temporal sliding windows helps generate more vivid videos, but this approach ignores long-range 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 5 demonstrate that FreeLong successfully renders temporal consistent longer videos, outperforming all other methods. To validate the effectiveness of each module in our FreeLong method-global 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 to 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 6 , we show the videos generated by combining the low-frequency 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.Our method can be seamlessly extended to 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 is shown running on the road, then standing, and finally flying into the sky, all within a consistent scene and with smooth action transitions. In the second row, we demonstrate a 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-grained details of each prompt transition.To examine the scalability of our FreeLong, we extend the video generation length beyond 128 frames. As depicted in Figure 8 , FreeLong effectively generates videos with even longer durations, such as 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, we introduced FreeLong, a 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 to high-frequency distortion. To resolve this issue, we employ the SpectralBlend Temporal Attention (SpectralBlend-TA) 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. FreeLong also supports coherent multi-prompt generation, offering a practical solution for high-quality long video creation without extensive retraining. 
broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes]Justification: In the appendix, we discussed our limitations, societal impact, and directions for future work.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.• The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [NA] Justification: The paper poses no such risks.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?Answer: [Yes] Justification: We cited related papers.Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. 
 
 Appendix: It is important to consider the potential ethical implications of our approach, which is typical in generative models. By incorporating Video Diffusion Model [1, 2] into our methodology, there is a chance that our system may also inherit the biases present in these models. Additionally, we need to be aware of the potential risks, including the generation of deceptive, harmful, or discriminatory content.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 represents a promising approach to training-free long-form text-to-video generation, offering significant improvements in consistency and fidelity despite these challenges. NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.We split the frequency components into high-frequency (ϕ ∼ (0.25π -1.00π)) and low-frequency (ϕ ∼ (0.00π -0.25π)) and compared the SNR of each component in long videos to the corresponding SNR in 16-frame videos. 


Decision: 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.

Reviews: review: paper_summary: paper_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.
strength_weakness: strength_weakness: 1. Inference time comparisons should include both single-pass and multi-pass temporal attention from previous methods to demonstrate the advantages clearly.
2. Although the video generation results are impressive, a more extensive user study is required to validate the consistency and quality of the generated videos.
questions: questions: Some concerns please refer to the Weaknesses. 
Also what about the longer duration of video generation, for example, videos longer than 128 frames?
limitations: limitations: Including examples of failure cases would provide a better understanding of the method's limitations.
paper_summary: paper_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.
strength_weakness: strength_weakness: My main concern lies in the computational cost.
questions: questions: 1) Computing the global attention map and video features seems computationally expensive, what is the computational cost compared to previous method (like freenoise) ?
2) Method like freenoise suffer from repetitive generation. Generated contents will repeat several times. Can freelong solve this problem?
limitations: limitations: Yes
paper_summary: paper_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.
strength_weakness: strength_weakness: 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.

2. 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.

3. 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?)
questions: 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).

2. Did you consider the memory cost of long-video generation with one forward pass (128 frames)?
limitations: limitations: Yes.
paper_summary: paper_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.
strength_weakness: strength_weakness: 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.
2. 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.
3. 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.
questions: 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?
2. 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: 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.

Comments: title: Thank you!
Thank you！
Thank you!
Thank you!
Good rebuttal
comment: Thank you for your thoughtful review and for recognizing our efforts to address your concerns. We sincerely appreciate your consideration and insights. Thank you once again!
Thank you for the authors' response. The rebuttal addresses most of my concerns. I would like to raise my score.
Thank you for your thorough review and constructive feedback. We greatly appreciate your thoughtful comments and are pleased that our response has addressed most of your concerns. We will highlight the expansion on other models in future versions. Regarding the demonstration of FreeLong's effectiveness in generating complex motions over longer sequences:

Due to the 50 MB limit of the one-page PDF, we could only provide a few images for each example. While these images demonstrate the generalizability of our approach, they may not fully capture the complexity of the motions FreeLong can produce. Videos offer more coherence and motion variation than images. We would like to highlight that, compared to previous methods like FreeNoise, FreeLong achieves consistent long video generation with more natural motions, as shown in Figure 3 of the one-page PDF.

In future versions, we will include more detailed examples to showcase FreeLong's capability to handle complex motions in longer sequences effectively.

Thank you once again for your valuable insights, which have been instrumental in enhancing our work.
Thank you for your thoughtful review and for recognizing our efforts to address your concerns. We are pleased to hear that you found our explanations on computational efficiency and reducing repetitive generation helpful. We sincerely appreciate your consideration and insights. Thank you once again!
Thank authors for the response. You have addressed my concerns.
Thank you for the comprehensive response from the authors, which has addressed most of my concerns; I believe the authors need to highlight the expansion on other models in subsequent versions; my only remaining concern is regarding the effectiveness, as I think the video results of long sequence generation shown in the PDF still do not fully demonstrate that FreeLong can effectively generate complete complex motions. I will raise the evaluation to 5.
We greatly appreciate the reviewer's prompt response and thoughtful evaluation of our work! Your positive feedback and constructive comments are truly valuable in helping us refine and improve our project. We would like to express our heartfelt gratitude for your time and consideration. Thank you once again!
Thank authors for the thorough response and the additional experiments results. I am satisfied with the rebuttal and no longer have any further concerns. Therefore, I have decided to raise my score to accept.