SF-V: Single Forward Video Generation Model

Authors: zhixing  zhang
yanyu  li
yushu  wu
yanwu  xu
anil  kag
ivan  skorokhodov
willi  menapace
aliaksandr  siarohin
junli  cao
dimitris  metaxas
sergey  tulyakov
jian  ren
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 17:22:31

Abstract: Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computational costs. In this work, we propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained video diffusion models. We show that, through the adversarial training, the multi-steps video diffusion model, i.e., Stable Video Diffusion (SVD), can be trained to perform single forward pass to synthesize high-quality videos, capturing both temporal and spatial dependencies in the video data. Extensive experiments demonstrate that our method achieves competitive generation quality of synthesized videos with significantly reduced computational overhead for the denoising process (i.e., around $23\times$ speedup compared with SVD and $6\times$ speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing.

Main paper: 
 introduction: 
 Video generation is experiencing unprecedented advancements by leveraging large-scale denoising diffusion probabilistic models [1, 2] to create photo-realistic frames with natural and consistent motion [3, 4] , revolutionizing various fields, such as entertainment and digital content creation [5, 6] .Early efforts on image generation show that diffusion models have the significant capabilities when scaled-up to generate diverse and high-fidelity content [1, 2] . Additionally, these models benefit from a stable training and convergence process, demonstrating a considerable improvement over their predecessors, i.e., generative adversarial networks (GANs) [7] . Therefore, many studies on video generation are built upon the diffusion models. Some of them utilize the pre-trained image diffusion models for video synthesis through introducing temporal layers to generate high-quality video clips [8, 9, 10, 11] . Inspired by this design paradigm, numerous video generation applications have emerged, such as animating a given image with optional motion priors [12, 13, 14, 15] , generating videos from natural language descriptions [16, 17, 5] , and even synthesizing cinematic and minuteslong temporal-consistent videos [18, 4] .Despite the impressive generative performance, video diffusion models suffer from tremendous computational costs, hindering their widespread and efficient deployment. The iterative nature of the sampling process makes video diffusion models significantly slower than other generative models (e.g., GANs [19, 20] ). For instance, in our benchmark, it only takes 0.3 seconds to perform a single denoising step using the UNet from the Stable Video Diffusion (SVD) [13] model to generate 14 frames on one NVIDIA A100 GPU, while consuming 10.79 seconds to run the UNet with the conventional 25-step sampling.The significant overhead introduced by iterative sampling highlights the necessity to generate videos in fewer steps while maintaining the quality of multi-step sampling. Recent works [21, 22, 23] extend consistency training [24] to video diffusion models, offering two main benefits: reduced total runtime by performing fewer sampling steps and the preservation of the pre-trained ordinary differential equation (ODE) trajectory, allowing high-quality video generation with fewer sampling steps (e.g., 8 steps). Nevertheless, these approaches still struggle to achieve single-step high-quality video generation.On the other hand, distilling image diffusion models into one step via adversarial training have shown promising progress [25, 26, 27, 28, 29] . However, scaling up such approaches for video diffusion model training to achieve single-step generation has not been well studied. In this work, we leverage adversarial training to obtain an image-to-vide o generation model that requires only single-step generation, with the contributions summarized as follows:• We build the framework to fine-tune the pre-trained state-of-the-art video diffusion model (i.e., SVD) to be able to generate videos in single forward pass, greatly reducing the runtime burden of video diffusion model. The training is conducted through adversarial training on the latent space.• To improve the generation quality (e.g., higher image quality and more consistent motion), we introduce the discriminator with spatial-temporal heads, preventing the generated videos from collapsing to the conditional image.• We are the first to achieve one-step generation for video diffusion models. Our one-step model demonstrates superiority in FVD [30] and visual quality. Specifically, for the denoising process, our model achieves around 23× speedup compared with SVD and 6× speedup compared with exiting works, with even better generation quality.Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes] Justification: Our main contributions and scope have been accurately summarized in the last paragraph of introduction. In Sec. 4, we conduct comprehensive experiments supporting the main claims made in the abstract and introduction.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or 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. 
related work: 
 Video Generation has been a long studied problem, aiming for high-quality image generation and consistent motion synthesis. Early efforts in this domain utilize adversarial training [31, 32] . Though extensively investigated, the trained models still suffer from low resolution, limited generated sequences, and inconsistent motion. Recent studies leverage denoising diffusion probabilistic models [1, 33, 34] to scale the video generators up to billions of model parameters, achieving high-fidelity generation sequences [35, 36, 37, 38, 39, 5, 4, 3, 18] . Nonetheless, the tremendous computation cost of video diffusion models hinders their wide deployment. It takes tens of seconds to generate a single video batch even for high-tier server GPUs. Consequently, the reduction of denoising steps [21, 40, 22] is pivotal to efficient video generation, which linearly scales down the total runtime.Step Distillation of Diffusion Models. Initially developed upon image diffusion models, progressive distillation [41, 42] aims to distill a less-step student mimicking the full-step counterpart. Specifically, at each step, the student learns to predict a teacher location in the ODE flow, resulting in fewer required denoising steps during inference time. Latent Consistency Models (LCM) [24, 43, 44, 45, 46, 47, 48] instead proposes to refine the prediction objective into clean data, and achieves high-fidelity generation with fewer (2 ∼ 4) steps. Rectified flow [49, 50] progressively straights the ODE flow where each denoising step becomes a substitution of a long trajectory. UFOGen [25] , ADD [27] , and its latentspace successor LADD [28] further incorporate adversarial loss to distill teacher signal into the few-step student, enabling one-step generation with reasonable quality, and outperforming the teacher model with about 4 steps. DMD [26] proposes to combine a distribution matching objective and a regression loss to distill a one-step generator. The recent SDXL-Lightning [29] combines progressive distillation with adversarial loss to mitigate the blurry generation issue and ease the convergence of multi-step settings. In addition, SDXL-Lightning refines the design of the discriminator and proposes two adversarial loss objectives to balance sample quality and mode convergence.When it comes to video models, VideoLCM [40] and AnimateLCM [21] adopt consistency distillation to enable 4-step generation with comparable quality to the full-step pre-trained video diffusion model. However, in the one-step setting, there are still considerable performance gaps observed for the visual quality. Animate-Diff Lightning [22] incorporates adversarial distillation to further reduce warps and blurs in the 1-2 step setting, despite that the model still underperforms full-step baselines.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: We discuss the limitations of our work in Sec. 5.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. 
method: 
 Our goal is to generate high-fidelity and temporally consistent videos in as few sampling steps as possible (i.e., 1 step). The adversarial objective has been proven effective in reducing the number of sampling steps required by diffusion models in image space [27, 28, 25, 51] . However, limited efforts have been conducted on scaling up the effective adversarial training to reduce the number of sampling steps for video diffusion models. In the following, we introduce the framework of latent adversarial training to obtain efficient video diffusion model by running sampling in single step.In this framework, we initialize the generator and part of the discriminator with the weights of a pre-trained video diffusion model. Moreover, we introduce a structure with separate spatial and temporal discriminator heads to enhance frame quality and motion consistency.Our method is built upon the Stable Video Diffusion (SVD) [13] , which is an implementation of the EDM-framework [33] for conditional video generation, where the diffusion process is conducted in latent space. We choose the publicly released image-to-video generation pipeline of SVD due to its superior performance in generating high-quality and motion-consistent videos.Training Diffusion Models with EDM. To facilitate the presentation, let p data (x 0 ) denote the data distribution and p(x; σ) represent the distribution obtained by adding σ 2 -variance Gaussian noise to the data. For sufficiently large σ max , p(x; σ max ) ≈ N (0, σ 2 max ). Starting from high variance Gaussian noise x M ∼ N (0, σ 2 max ), the diffusion models sequentially denoise towards σ 0 = 0 through the numerical simulation of the Probability Flow ODE [52] . The denoiser, D θ , attempts to predict the clean x 0 and is trained via denoising score matching:EQUATIONwhere p(σ, n) = p(σ)N (n; 0, σ 2 ), p(σ) is a distribution over noise levels σ, and λ σ : R + → R + is a weighting function.EDM [33] parameterizes the denoiser D θ as:EQUATIONwhere F θ is the network to be trained. The preconditioning functions are set as We initialize our generator and discriminator using the weights of a pre-trained image-to-video diffusion model. The discriminator utilizes the encoder part of the UNet as its backbone, which remains frozen during training. We add a spatial discriminator head and a temporal discriminator head after each downsampling block of the discriminator backbone and only update the parameters of these heads during training. Given a video latent x 0 , we first add noise σ t through a forward diffusion process to obtain x t . The generator then predicts x0 given x t . We calculate the reconstruction loss L recon between x 0 and x0 . Additionally, we add noise level σ ′ t to both x 0 and x0 to obtain real and fake samples, x ′ t and x′ t . The adversarial loss L adv is then calculated using these real and fake sample pairs.c skip (σ) = (σ 2 +1) -1 , c out (σ) = -σ √ σ 2 +1 , c in (σ) = 1The training of video model asks for a dataset of videos, each consisting of N frames with height H and width W . Given a video V 0 = {I i 0 } N i=0 , where I i 0 ∈ R 3×H×W , SVD [13] maps each frame separately to latent space using a frame encoder, E. The encoded frames are represented as x 0 = {E(I i 0 )} N i=0 , resulting in x 0 ∈ R N ×4× H× W . Here, x 0 ∼ p data (x 0 ) is a sequence of N latent frames with 4 channels, height H, and width W . SVD inflates a text-to-image diffusion model to a text-to-video diffusion model [10] . The text conditioning is replaced with image conditioning to create an image-to-video diffusion model. Consequently, the parameterized denoiser D θ in Eq. ( 2) is modified as follows:EQUATIONwhere c is the image condition I 0 0 , i.e., the first frame of the video. At sampling time, D θ is leveraged to restore x t-1 from x t using the following relation [33] :EQUATIONwhere σ t is obtained withEQUATION)where T is the total number of denoising steps and ρ is a hyper-parameter controlling the emphasis level to low noise levels.Design of Networks. Diffusion-GAN hybrid models are designed for training with large denoising step sizes [25, 27, 28, 51] . Our training procedure, illustrated in Fig. 2 , involves two networks: a generator G θ and a discriminator D ϕ . The generator is initialized from a pre-trained UNet diffusion model with weights θ (i.e., the UNet from SVD). The discriminator is partially initialized from a pretrained UNet diffusion model. Namely, the backbone of the discriminator shares the same architecture and weights as the pre-trained UNet encoder, and the weights of this backbone are kept frozen during training. Additionally, we augment the discriminator by adding a spatial discriminator head and a temporal discriminator head after each backbone block. Therefore, in total, the discriminator comprises four spatial discriminator heads and four temporal discriminator heads. Only the parameters in these heads are trained during the discriminator training steps. The detailed architecture of these heads will be further discussed in Sec. 3.3.Training. We use a pair of generated samples x0 and real samples x 0 to conduct the adversarial training. Specifically, during training, the generator G θ produces generated samples x0 (x t ; σ t , c) from noisy data x t . The noisy data points are derived from a dataset of real latents x 0 via a forward diffusion process x t = x 0 + σ t ϵ. We sample σ t uniformly from the set {σ 1 , • • • , σ Tg-1 }, obtained by setting T to T g and t ∈ {1, 2, • • • , T g -1} in Eq. ( 5). In practice, we set T g = 4. The generated sample x0 is given by:EQUATIONTo train the discriminator, we forward the generated samples x0 and real samples x 0 into it, aiming to let the discriminator distinguish between them. However, for a more stabilized training, inspired by exiting works [28] , we add noise to the samples before passing them to the discriminator, since the backbone of the discriminator is initialized from a pre-trained UNet with weights frozen during training. Namely, we sample 5), according to a discretized lognormal distribution defined as:σ ′ t from the set {σ ′ 1 , • • • , σ ′ T d -1 }, obtained by setting T to T d and t ∈ {1, 2, • • • , T d -1} in Eq. (EQUATIONwhere P mean and P std control the noise level added to the samples before passing them to the discriminator. A visualization of how different P mean and P std affect the probability of σ ′ sampled is illustrated in Fig. 6 . In practice, we set T d = 1, 000. We diffuse the real and generated samples through the forward process to obtain [27, 53, 54] , we use the hinge loss [55] as the adversarial objective function for improved performance. The adversarial optimization for the generator L G adv (x 0 , ϕ) is defined as:x′ t = x0 + σ ′ t ϵ and x ′ t = x 0 + σ ′ t ϵ, respectively. Following literatureEQUATIONFurthermore, we notice that a reconstruction objective, L recon , between x 0 and x0 can significantly improve the stability of the training process. We use Pseudo-Huber metric [56, 43] for reconstruction loss, as:EQUATIONwhere c > 0 is an adjustable constant. Thus, the overall objective for training the generator is as follows with λ balances two losses:EQUATIONOther other hand, the discriminator is trained to minimize:EQUATIONwhere R1 denotes the R1 gradient penalty [57, 27] . Here, we omit other conditional input for D ϕ , such as c noise (σ ′ ) and image conditioning c, for simplicity.Discussion. Our latent adversarial training framework is largely inspired by LADD [28] . Similar to LADD, we set T g = 4 in practice and utilize a pre-trained diffusion model as part of the discriminator. However, our approach has several key differences compared with LADD [28] . First, we extend the image latent adversarial distillation framework to the video domain by incorporating spatial and temporal heads to achieve one-step generation for video diffusion models. The specifics of the spatial and temporal heads are discussed in Sec. 3.3. Second, based on the EDM-framework [33] , we observe that sampling t ′ using a discretized lognormal distribution provides more stable adversarial training compared to the logit-normal distribution used in LADD [28] . Finally, unlike LADD [28] , we utilize real video data instead of synthetic data for training and incorporate a reconstruction objective (i.e., Eq. ( 9)) to ensure more stable training.Temporal Head Per-pixel hinge loss Our discriminator heads take in intermediate features of the UNet encoder. Follow exiting arts [54, 53] , we use image conditioning and frame index as the projected condition c. Left: For spatial discriminator heads, the input features are reshaped to merge the temporal axis and the batch axis, such that each frame is considered as an independent sample. Right: For temporal discriminator heads, we merge spatial dimensions to batch axis. Table 1 : Comparison Results. We compare our method against SVD [13] , AnimateLCM [21] , UFOGen [25] , and LADD [28] using different numbers of sampling steps. AnimateLCM * indicates the usage of the officially provided 25-frame model, with only the first 14 frames considered for FVD calculation. † indicates our implementations. We also report the latency of the denoising process for each setting, measured on a single NVIDIA A100 GPU. To train the discriminator for better understanding of the spatial information and temporal correlation, we employ separate spatial and temporal discriminator heads for adversarial training [31, 32] . The backbone of the discriminator is the encoder from the pre-trained diffusion model (i.e., UNet), which consists of four spatial-temporal blocks sequentially [10] . The first three blocks downsample the spatial resolution by a factor of 2, and the last block maintains the spatial resolution. We extract the output features from each spatial-temporal block and utilize a spatial head and a temporal head to determine whether the sample is real or fake. The discriminator can be conditioned on additional information via projection [58] to enhance performance. In our setting, we use the image condition c and σ ′ as the projected condition C.Spatial Head. For an input feature of shape b × t × c × h × w, the spatial discriminator first reshapes it to (bt) × c × h × w. This way, each frame feature in a video is processed separately. The architecture for our proposed spatial head is illustrated in the left part of Fig. 3 .Temporal Head. Even though the features obtained from the discriminator backbone contain spatialtemporal information, we observe that using only spatial discriminator heads causes the generator to produce frames that are all identical to the image condition. To achieve better temporal performance (e.g., more vivid motion), we propose to add a temporal discriminator head parallel to the spatial discriminator head. The input features are reshaped to (bhw) × c × t instead. The architecture for our temporal head is illustrated in the right part of Fig. 3 .Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: As we mentioned in the abstract, we plan to release the code and pre-trained models with sufficient instructions to faithfully reproduce our results. Guidelines:• The answer NA means that paper does not include experiments requiring code. 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). 
experiment: 
 Implementation Details. We apply Stable Video Diffusion [13] as the base model across our experiments. All the experiments are conducted on an internal video dataset with around one million videos. We fix the resolution of the training videos as 768 × 448 with the FPS as 7. The training is conducted for 50K iterations on 8 NVIDIA A100 GPUs, using the SM3 optimizer [59] with a learning rate of 1e -5 for the generator (i.e., UNet) and 1e -4 for the discriminator. We set the momentum and β for both optimizers as 0.5 and 0.999, respectively. The total batch size is set as 32 using a 4 steps gradient accumulation. We set the EMA rate as 0.95. We set P mean = -1, P std = -1, and λ = 0.1 if not otherwise noted. At inference time, we sample videos at resolution of 1024 × 576.To comprehensively evaluate the capabilities of our method, we use SDXL [60] (with refiner) to generate images of different scenes at the resolution of 1024 × 1024. We then perform center crop on the generated images to get resolution as 1024 × 576, which serves as the condition of our approach to synthesize videos of 14 frames at 7 FPS. As shown in Fig. 4 , our method can successfully generate videos of high-quality frames and consistent object movements with only 1 step during inference.Quantitative Comparisons. We present a comprehensive evaluation of our method compared to the existing state-of-the-art approach, AnimateLCM [21] , UFOGen [25] , LADD [28] , and SVD [13] . To , AnimateLCM [21] , LADD [28] , UFOGen [25] , and Our Approach. We provide the synthesized videos (sampled frames) under various settings for different approaches. We use SVD to generate videos under 25, 16, and 8 sampling steps, AnimateLCM to synthesize videos under 4 sampling steps, LADD and UFOGen to generate videos under 1 sampling step. AnimateLCM, LADD and UFOGen generates blurry frames with few-steps and single-step sampling. Our approach can accelerate the sampling speed by 22.9× compared with SVD while maintaining similar frame quality and motion consistency.conduct a fair comparison on the SVD model, we train the AnimateLCM, UFOGen, and LADD on SVD using our video dataset. We follow the released code and instructions provided by AnimateLCM authors. Additionally, we include the officially released AnimateLCM-xt1.1 [21] by evaluating the first 14 generated frames and denote the approach as AnimateLCM * . We try our best to implement LADD [28] and UFOGen [25] and denote respectively as LADD † , and UFOGen † . Note that simply re-using the discriminator from LADD [28] and UFOGen [25] leads to out-of-memory issue, since the computation in the video model is much larger than the image model. Here we replace the discriminator from LADD [28] and UFOGen [25] with the one proposed in our work.We follow exiting works [61] by using Fréchet Video Distance (FVD) [30] as the comparison metric. Specifically, we use the first frame from the UCF-101 dataset [62] as the conditioning input and generate 14-frame videos at a resolution of 1024 × 576 at 7 FPS for all methods. The generation results are then resized back to 320 × 240 for FVD calculation. Our method is compared against SVD [13] and AnimateLCM [21] , each using a different number of sampling steps. Furthermore, to better demonstrate the effectiveness of our method, we measure the generation latency of each method, which is calculated on running the diffusion model (i.e., UNet). Note that only for SVD [13] , classifier-free guidance [63] is used, leading to higher computational cost.As shown in Tab. 1, our method achieves comparable results to the base model using 16 discrete sampling steps, resulting in approximately a 23× speedup. Our method also outperforms the 8steps sampling results for AnimateLCM and AnimateLCM * , indicating a speedup of more than 6×. For single-step evaluation, our method performs much better than existing step-distillation methods [25, 28] built upon image-based-diffusion models.Qualitative Comparisons. We further provide qualitative comparisons across different approaches by using publicly available web images. Fig. 5 presents generation results from SVD [13] with 25, 16, and 8 sampling steps, AnimateLCM [21] with 4 sampling steps, UFOGen [25] , LADD [28] , and our method with 1 sampling step. As can be seen, our method achieves results comparable to the sampling results of SVD using 16 or 25 denoising steps. We notice significant artifacts for videos synthesized by SVD when using 8 denoising steps. Compared to AnimateLCM [21] ,UFOGen [25] ,and LADD [28] , our method produces frames of higher quality and better temporal consistency, with fewer or same denoising steps, demonstrating the effectiveness of our proposed approach.Effect of Discriminator Heads. We explore the effect of our proposed spatial and temporal heads by measuring the FVD on the UCF-101 dataset. We conduct latent adversarial training with three different discriminator settings to analyze the impact of our spatial and temporal discriminators. As shown in Tab. 2, training with only spatial heads (denoted as SP) or only temporal heads (denoted as TE) results in significantly worse performance than using all of them (denoted as SP+TE).Nevertheless, since our discriminator backbone shares the same architecture as the spatial-temporal generator, the receptive field of each pixel on the feature maps provided by the backbone can cover a region both spatially and temporally. Additionally, we embed the frame index as an additional projected condition. Consequently, even when using only spatial heads or only temporal heads, the generated videos still exhibit reasonable frame quality and temporal coherence.Effect of Noise Distribution for Discriminator. As shown in Fig. 6 , following Eq. ( 5), P mean and P std control the distribution of σ ′ t , which is the noise level added to x 0 or x0 before passing to the discriminator as real and fake samples, respectively. We explore the effect of different noise distributions on model performance by calculating FVD on the UCF-101 dataset.When the sampled σ ′ t is concentrated on small values, e.g., P mean = -2 and P std = -1 in our case, we notice that the discriminator can quickly learn to distinguish real samples from fake ones. This leads to a significant drop in performance, as shown in Tab. 3 and Fig. 7 .On the other hand, when the noise level becomes too high, e.g., P mean = 1 andP std = 1, the discriminator input, which is c in (σ ′ t )x ′ t = x0+σ ′ t ϵ √ σ ′ t 2 +1, results in small adversarial gradients for the generator. This causes increased artifacts in the generated videos, as shown in Fig. 7 and Tab. 3. 𝑃 !"#$ = -2, 𝑃 %&' = -1 𝑃 !"#$ = -1, 𝑃 %&' = -1 𝑃 !"#$ = 0, 𝑃 %&' = 1 𝑃 !"#$ = 1, 𝑃 %&' = 1 
discussion and conclusion: 
 In this work, we leverage adversarial training to reduce the denoising steps of the video diffusion model and thus improve its generation speed. We further enhance the discriminator by introducing spatial-temporal heads, resulting in better video quality and motion diversity. We are the first to achieve 1-step generation for video diffusion models while preserving comparable visual quality and FVD scores, democratizing efficient video generation to a broader audience by delivering more than 20× speedup for the denosing process. 
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: [Yes] Justification: We discuss this in the broader impacts.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: Our experiments involve models from Stable Video Diffusion [13] and Ani-mateLCM [21] . Both works are properly cited in the paper. We also specially mention the version of each model we use in the paper.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. 
 
 Appendix: Ours (1 step)Figure 8 : Limitations. We show that, for some conditional images, our model tends to generate a few unsatisfactory frames when complex motion might be required (Second Row). Similar artifacts can also be observed in frames generated from SVD by sampling at 25-steps (First Row).Limitations. We observe that when the given conditioning image indicates complex motion, e.g.running, our model tends to generate unsatisfactory results, e.g.blurry frames, as shown in Fig. 8 . Such artifacts are introduced by the original SVD model, as can be observed in Fig. 8 . We believe a better text-to-video model can solve such issue.This work successfully achieves single sampling step for video diffusion models. However, under such setting, the temporal VAE decoder and the encoder for image conditioning take a considerable portion of the overall runtime. We leave the acceleration of these models as future work.NeurIPS Paper Checklist 


Decision: This paper has proposed an approach to perform image-to-video inference in a single step through training a GAN model through distillation. The presented approach has strong practical applications, and the idea is simple but works very well. Although the average rating is less than 5, The AC believes all major concerns are already addressed by the rebuttal. The experimental results are compelling, and the evaluation is comprehensive. The AC also examined some issues that reviewers may have overlooked and believes the raised concerns should have been solved. Thus, the AC recommends the acceptance of the paper. The decision of this paper has been discussed with the SAC.

Reviews: review: paper_summary: paper_summary: The paper proposes an idea of training a distillation approach using GAN based technique. The advantage which is suggested by the authors is that such distillation approach can reduce the computational cost associated with the sampling new samples during the inference time. Instead of taking multiple sampling steps only single step is required for inference.
strength_weakness: strength_weakness: - Videos do not look like they're real world videos. How does this work with the real world videos.

- The motion is very laminar in the videos. How is the performance when trained on videos with non laminar motion.
questions: questions: - GANs suffer from mode collapse problem how was it ensured that is not the case here with this approach?

- GANs suffer from the problem of instability in training how was it handled 

- Why weren't standard video datasets like UCF101 used in evaluation?
limitations: limitations: GAN based models suffers from training instability because of the presence of adversarial loss.
paper_summary: paper_summary: The authors propose a method to generate similar-quality as the original video diffusion model Stable Video Diffusion (SVD) in a single feedforward pass.
To this end, they take the pre-trained SVD model and fine-tune it with a reconstruction and adversarial loss. The discriminator uses a frozen copy of the SVD encoder along with trained spatial & temporal readout heads to encourage both spatial and temporal consistency. Further, the authors add noise to the inputs of the discriminator to stability training.

The results are of slight better quality than 16 steps of SVD, and slightly worse than 25 steps of SVD.
strength_weakness: strength_weakness: - A large chunk of the methods section repeats how the original diffusion model is trained -- since this is already known information and not directly relevant to the presented method, it could be shortened or moved to the appendix
- The proposed method is fairly simple and incremental, though the results are better than existing methods
questions: questions: - L257: what's the relative compute / time that goes into the encoder / decoder compared to the actual latent model?
limitations: limitations: The authors briefly discuss the fact that the VAE decoder & encoder for image conditioning.

It should be noted that training GANs can often come with the additional headache of instabilities.
paper_summary: paper_summary: The paper tackles the task of distilling diffusion-based text-to-video models into single-step models, achieving much higher sampling speeds.  To this end, they build a framework to fine-tune a pretrained video diffusion model. This fine-tuning is done in an adversarial setting in the latent space, whereby the generator and discriminator are designed to achieve higher image quality and video consistency. In particular, the generator is initialized from a pretrained video model, and the discriminator is partially initialized with the layers of a pretrained encoder, where additional spatial-temporal layers are added and trained. The model achieves SOTA SVD quality in the setting of 1-step video diffusion and an additional speedup compared to existing art.
strength_weakness: strength_weakness: My main concerns are concerning the novelty and significance of the proposed approach in the following sense:

1. One component of the method is a re-design of the generator and discriminator. First, this in itself seems to be of limited novelty. The proposed components are standard and well-established in the community. Finetnuning the discriminator by adding additional layers is not new, and similarly, for the generator, copying the pretrained encoder seems sensible but does not offer a new contribution. Second, one could take the existing single-step adversarial text-to-image approaches and replace the generator and discriminator with the architecture proposed in this paper. The authors identify such papers well (e.g. [24, 25, 26, 27, 28]) but do not compare them to such a baseline. Hence, assessing whether the proposed architectural changes are meaningful is difficult. 

2. The proposed training regime (adversarial training and reconstruction losses) is another component. On the flip side to 1, is this training regime important? In that sense, one could take existing single or multi-step video diffusion baselines (e.g., [14, 21]) and change the training regime to that proposed in this paper. Further, assuming that this training regime improves in the video setting, one could assume that it is more general and applicable also in the single-step text-to-image regime. 

Further, the proposed components both seem to be marginally novel. Adversarial training for single-step distillation and the temporal components are also not new. Is there something non-trivial in the combination of both components?
questions: questions: I believe the paper provides a great engineering effort, significantly improving sampling speed and quality compared to the state-of-the-art. However, I am concerned about the novelty of the proposed approach, as indicated in the weaknesses section.
limitations: limitations: Limitations are briefly addressed but not sufficiently analyzed. It would be great to see examples of errors introduced by the approach or some further analysis.
paper_summary: paper_summary: This paper proposes a method to accelerate video generation model inference speed by distilling the multi-step reasoning of Singular Value Decomposition (SVD) into a single-step generation using adversarial networks. This approach achieves comparable results to multi-step SVD generation while significantly improving inference efficiency. Experimental results validate the effectiveness of the authors' method.
strength_weakness: strength_weakness: 1. The authors trained the entire network using 1 million internal videos. For those without access to this dataset, reproducing this work could be challenging. Will the authors make these data and source code publicly available?
2. How many training data points are needed to ensure that the distilled model has strong generalization capabilities? The training process for SVD likely requires far more than 1 million data. Is using 1 million data for distillation training sufficient?
3. The author mentioned using the encoder of a Unet for initializing the discriminator. What is the insight behind this choice? Why did they consider this initialization method?
4. In Adversarial Diffusion Distillation, they use distillation loss to transfer knowledge from the teacher model to the student model. Why did the authors not use this approach here? Is there any specific reason?
questions: questions: see Weaknesses
limitations: limitations: It's unclear how well the method generalizes currently, as the authors only conducted quantitative analysis on the UCF101 dataset. This uncertainty arises because the authors trained the model using only 1 million data points.

Comments: comment: Dear Reviewer Do55,

We would like to thank you again for your valuable feedback on our paper.

As the period for the Author-Reviewer discussion is closing today, we would like to use this opportunity to kindly ask if our responses sufficiently clarify your concerns. We sincerely appreciate your time and consideration.

Best,

Authors
Dear Reviewer R1mD,

We would like to thank you again for your valuable feedback on our paper.

As the period for the Author-Reviewer discussion is closing today, we would like to kindly ask if our responses have sufficiently addressed your concerns. We sincerely appreciate your time and consideration.

Best,

Authors
Dear Reviewer R1mD,

We would like to thank you again for your valuable feedback on our paper.

As the period for the Author-Reviewer discussion is closing very soon, we would like to use this opportunity to kindly ask if our responses sufficiently clarify your concerns. We sincerely appreciate your time and consideration.

Best,

Authors
Dear Reviewer Q9Lc,

Thank you for reading our rebuttal and providing a response! If there are any remaining issues you would like us to address, we are always willing to provide more clarification.

Best,

Authors
Thank you for the thorough response. The rebuttal has addressed most of my concerns. I am happy to keep my initial score.
Dear Reviewer FT7s,

Thank you for reading our rebuttal and providing response!

We would like to apologize that our previous response in Q1 regarding the comparison between our approach and LADD does not contain the detailed explanation regarding the experiments that we have done. In the following, we connect our previous experiments along with the difference between our work and LADD. 

First, in Tab. 2 of the main paper, we conduct ablation experiments with different discriminator heads settings. We show that, using spatial and temporal heads achieves better generation quality than only using spatial head (which is the design from LADD). Additionally, our discriminator only uses certain part of the UNet that we find important, while LADD does not. In this way, we can significantly reduce the training computation for adversarial training, and train the video model much faster and consume much less GPU memory. By directly using the discriminator design from LADD, we notice the out of GPU memory issue on 80G Nvidia A100 GPU.

Second, based on the EDM-framework, we observe that sampling $t^\prime$ using a discretized lognormal distribution provides more stable adversarial training. In Fig. 6 and Fig. 7 of the main paper, we show the importance of our proposed noise sampling schedule for achieving the better performance.

Third, we utilize real video data instead of synthetic data for training, removing the computation burden for calculating and saving the synthetic data. For instance, generating 1M videos using SVD requires approximately 3K GPU hours.

Fourth, we employ reconstruction objective (Eq. 9 in the main paper) along with R1 gradient penalty (Eq. 11 in the main paper) to stablize the training of the video diffusion model. Without R1 gradient or the reconstruction objective, we observe frequent training divergence.

Besides, as mentioned in our answer for Q2, we use the proposed training regime from LADD [27] combined with the discriminator proposed in our work and conduct quantitative and qualitative comparison experiments. Our method significantly outperforms LADD [27] training regime in the video distillation task, demonstrating the effectiveness of our proposed training regime. 

Thanks again for the feedback from the reviewer. We will add the above discussion to the revised paper and hope it can be helpful to understand the difference between our work and LADD.  We would deeply appreciate it if the reviewer could reconsider the score and we are always willing to address any of your further concerns.

Best,

Authors
I want to thank the authors for their rebuttal. 

While I still believe the novelty is limited, the additional evaluation has provided stronger evidence of the merit of the proposed approach compared to prior work. 

I still have some concerns about novelty, as noted in the author's answer (Q1). The authors note a number of differences from previous work LADD [27]. However, without ablating each of the mentioned components either with respect to speed or to generation quality, it is difficult to assess the importance of each component. I would appreciate it if the authors could address this.
Dear Reviewer Q9Lc,

Thank you for your valuable feedback on our submission.

We have provided additional explanations to address the key points you raised, including reproducing our results, training data, effectiveness of our discriminator initialization, reasons why we don’t use distillation loss, and the generalization ability of our method. As the deadline for the Author-Reviewer discussion is approaching, we would like to kindly ask if our responses sufficiently clarify your concerns or if there are any remaining issues you would like us to address. We appreciate your time and consideration.

Best,

Authors
Dear Reviewer FT7s,

Thank you for your valuable feedback on our submission. 

We have provided additional explanations to address the key points you raised, including our novelty, further comparisons, importance of our training regime, and limitations of our method. As the deadline for the Author-Reviewer discussion is approaching, we would like to kindly ask if our responses sufficiently clarify your concerns or if there are any remaining issues you would like us to address. We appreciate your time and consideration.

Best,

Authors
Dear Reviewer Do55,

Thank you for your valuable feedback on our submission. 

We have provided additional explanations to address the key points you raised, including our approach, inference time, and training instabilities. As the deadline for the Author-Reviewer discussion is approaching, we would like to kindly ask if our responses sufficiently clarify your concerns or if there are any remaining issues you would like us to address. We appreciate your time and consideration.

Best,

Authors
Dear Reviewer R1mD,

Thank you for your valuable feedback on our submission. 

We have provided additional explanations to address the key points you raised, including the generation of real-world videos, laminar motion, and issues related to instability during training and evaluation metrics. As the deadline for the Author-Reviewer discussion is approaching, we would like to kindly ask if our responses sufficiently clarify your concerns or if there are any remaining issues you would like us to address. We appreciate your time and consideration.

Best,

Authors