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 N. Metaxas
Sergey Tulyakov
Jian Ren
Venue: NeurIPS 2024 poster (2024)
Keywords: Video generation
diffusion model
distillation
adversarial training
Primary area: diffusion_based_models
TL;DR: Our model can generate high-quality and motion consistent videos by only performing the sampling once during inference.

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.

SF-V: Single Forward Video Generation Model
Zhixing Zhang
1.2+ 1.2+
Yanyu Li'
Yushu Wu'
Yanwu Xu'
Anil Kag'
Ivan Skorokhodov'
Willi Menapace'
Aliaksandr Siarohin'
Junli Cao
Dimitris Metaxas"
Sergey Tulyakov' Jian Ren
*Snap Inc.
2 Rutgers University
Project Page: http:://naa..rsaaachhhhhbbb.../s
Figure Example generation results from our single-step image-to-vidd video model. Our model can generate high-quality and motion consistent videos by only performing the sampling once during inference. Please refer to our webpage for whole video sequences.
Abstract
Diffusion-bari 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 novel approach 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, iee. Stable Video Diffusion (SVD), can be trained to perform single forward pass 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 23x speedup compared with SVD and speedup compared with existing works, with even better generation quality). paving the way for real-time video synthesis and editing.
'Work done during an internship at Snap Inc. Cooresponning author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Introduction
Video generation is experiencing unprecedented advancements by leveraging large-scale denoising diffusion probabillistic 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 stable training and convergence process, demonstrating 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 einematic and minutes- long temporal-consisten 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, 20J). 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 generate 14 frames on one NVIDIA A100 GPU, while consuming 10.79 seconds to run the UNet with the conventional 25 stes 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 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 follows:
We build the framework fine-tune the pre-trained state-of-the-ar 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 conducted through adversarial training 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 23x speedup compared with SVD and 6x speedup compared with exiting works, with even better generation quality.
2 Related Work
Video Generation has been 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 mod- els [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

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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 distill a less-step student mimicking the full--tep counterpart. Specifically, at each step, the student Iearns 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 ~ 4) steps. Rectified flow [49, 50] progressively straights the ODE flow where each denoising step becomes a substitution of long trajectory. UFOGen [25], ADD [27], and its latent- space 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 distribution matching objective and regression loss to distill a one-step generator. The recent SDXII-Ligttnnng [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 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.
3 Method
Our goal is to generate high-fidelity and temporally consistent videos in few sampling steps as possible (i.e., 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 pre-trained video diffusion model. Moreover, we introduce a structure with separate spatial and temporal discriminator heads to enhance frame quality and motion consistency.
3.1 Preliminaries of Stable Video Diffusion
Our method is built upon the Stable Video Diffusion (SVD) [13], which is 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 its superior performance in generating high-quality and motion-consistent videos.
Training Diffusion Models with EDM. To facilitate the presentation, let Pdata denote the data distribution and p(z; or) represent the distribution obtained by adding o2-variance Gaussian noise to the data. For sufficiently large maxx p(æ; maxx) x (O, of Starting from high variance Gaussian noise TMs ~ (0, o the diffusion models sequentially denoise towards do = 0 through the numerical simulation of the Probability Flow ODE [52]. The denoiser, Do, attempts to predict the clean Vn and is trained via denoising score matching:
E Exompante (ra)
Do + o) - -0o1
(1)
where p(a, n) = p())W 0, )2)))(())))))))))))))))))))))) p(o) is distribution over noise levels a, and > : RR - R- is
weighting function.
EDM [33] parameterizes the denoiser Dg as:
Do(x;o) = (o)s + Coua(a) Fo (C (a)):; Cnoise (o)),
(2)
where Fy is the network to be trained. The preconditioning functions are set as Cokip(a) = (a2+1)-11
(o) Vo"4i (a) vo"4i" and Cnoise (ar) = 0.25 logar
-g
3

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Generator
Discriminator
Pre-trained Weights
g
D の
Spatial Heads
Temporal Heads
Fake
Real
Ladv
Lrccon
diffuse
diffuse
x
diffuse of
Figure 2: Training Pipeline. 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 its backbone, which remains frozen during training. We add 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 Fo. we first add noise through forward diffusion process to obtain The generator then predicts 2o given zt. We calculate the reconstruction loss frecon between 2h and dn- Additionally, we add noise level of to both Zo and Lo to obtain real and fake samples, 2; and i. The adversarial loss Lado is then calculated using these real and fake sample pairs.
Stable Video Diffusion. The training of video model asks for dataset of videos, each consisting of N frames with height and width W. Given a video Vo = {T) where IJ E R3xH* SVD [13] maps each frame separately to latent space using a frame encoder, E. The encoded frames are represented as Ty = {E(T) J resulting in Zig E R-Nx4x8xX Here, Ty ~ Pdota((r)) is sequence N latent frames with 4 channels, height H, and width W.
SVD inflates 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 Da in Eq. (2) is modified as follows:
Do(2; c) (a))pp(a) x + Cout (a) Fo (Can (a)ar Cnoise (a), c),
(3)
where is the image condition 13, i.e., the first frame of the video.
At sampling time, Do leveraged to restore It-1 from 2t using the following relation [33]:
(x, Dø( te c))//rii Ft-1 = It (ar-1 ox)) de.
(4)
where o; is obtained with
o,
(0 1/0 min
+
1/p max
1/2)),
(5)
where T is the total number of denoising steps and is a hyper-paramett controlling the emphasis level low noise levels.
3.2 Latent Adversarial Training for Video Diffusion Model
Design of Networks. Diffusion-Gai GAAN hybrid models are designed for training with large denoising step sizes [25, 27, 28, 51]. Our training procedure, illustrated Fig. 2, involves two networks: generator Go and a discriminator D The generator is initialized from pre-trained UNet diffusion model with weights 0 (i.e., the UNet from SVD). The discriminator is partially initialized from a pre- trained 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 spatial discriminator head and

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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.
Latent Adversarial Training. We use a pair of generated samples žn and real samples zo to conduct the adversarial training. Specifically, during training, the generator Go produces generated samples (o( or, c) from noisy data 2t. The noisy data points are derived from a dataset of real latents 2o via forward diffusion process F4 IN + dCC. We sample or uniformly from the set {01, or, } obtained by setting T to Tg and (1,2,.. TT }} in Eq. (5). In practice, we set Tg 4. The
generated sample Sig is given by:
to (2 oe, Cskip (oxipp(=)).u (uaa + (ot (Cin (ot) Gnoise (ox), c).
(6)
To train the discriminator, we forward the generated samples do and real samples zo 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 of from the set {or, ,', TT J. obtained by setting T to Ta and discretized lognormal distribution defined as:
E (1, Ta 1 in Eq. (5), according
p(ot) & erf log(o) - ('mmoon (
-erf erf log(d)_1 -Pmean)
V2Patd
V2Psid
where Pmeen and Patd control the noise level added to the samples before passing them to the discriminator. A visualization of how different Pmean and Pata affect the probability of o' sampled is illustrated in Fig. 6. In practice, we set Ta 1, 000. We diffuse the real and generated samples through the forward process to obtain Y = Po + ofe and x = Dg + o.e, respectively.
Following literature 53, 54], we use the hinge loss [55] as the adversarial objective function for improved performance. The adversarial optimization for the generator Cl (ão, 6) is defined as:
Co Eg,a' mo Do (Cin (omm())))))))
8
Furthermore, we notice that reconstruction objective, Crecons between To and fo can significantly improve the stability of the training process. We use Pseudo-Huber metric [56, 43] for reconstruction loss, as:
Lrcoon Crecon(@os.) (o) = / |ão xol12 + c² -
where > O is adjustable constant. Thus, the overall objective for training the generator is as follows with À balances two losses:
C C + Xrrecon (fo, mo).
(10)
Other other hand, the discriminator is trained to minimize:
Ldde Teg Teo (max 1 DD (Cin (o() + +yR1]+ [o 1 DD (cm((())))) (11)
where R1 denotes the R1 gradient penalty [57, 27]. Here, we omit other conditional input for Dø- such as Caoise (o') and image conditioning c, for simplicity.
Discussion, Our latent adversarial training framework is largely inspired by LADD [28]. Similar to LADD, we set Tú = 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 achieve 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 I' 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)) ensure more stable training.
5

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Table 1: Comparison Results. We compare our method against SVD [13], AnimateLCM [21], UFOGen [25], and LADD [28] using different numbers of sampling steps. Miimatel.CM' indi- cates the usage of the officially provided 25-frame model, with only the first 14 frames considered for FVD calculation. i indicates our implementa- tions. We also report the latency of the denoising process for each setting, measured on a single
[b 1) hhhww
la vo) cl
Conv2D
ConviD
Affine
Affine
ResBlock2D
ResBlock1D
Conv2D
ConviD
NVIDIA A100 GPU.
Name
FVD
Steps
Latency (s)
Spatial Head Per-pixel hinge loss Temporal Head
153.4
25
10.79
Figure 3: Spatial & Temporal Discriminator Heads. Our discriminator heads take interme- diate features of the UNet encoder. Follow exit- ing 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 dis- criminator heads, we merge spatial dimensions to batch axis.
194.4
16
6.89
SVD [13]
488.6
3.44
1687.0
4
1.72
321.1
3,25
AnimateLCM" [21]
403.2
1.62
521.9
2
0.82
281.0
1.85
Animatel.CM [21]
801.4
0.92
1158.4
0.46
UFOGen [25]
1917.2
0.30
LADD [28]
1893.8
0.30
Ours
180.9
0.30
3.3 Spatial Temporal Heads
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 backhone of the discriminator is the encoder from the pre-trained diffusion model (i.e., UNet), which consists of four spatial resolution by a factor of and the last block maintains the spatial resolution. We extract the output features from each 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 o' as the projected condition C.
blocks sequentially [10]. The first three blocks downsample the
block and utilize spatial head and a temporal head to
Spatial Head. For an input feature of shape b x t x c x x to (bt) x h x This way. each frame feature in video is processed separately. The architecture for our proposed spatial head is illustrated in the left part of Fig. 3.
w, the spatial discriminator first reshapes
Temporal Head. Even though the features obtained from the discriminator backbone contain spatial- temporal 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) x c & instead. The architecture for our temporal head is illustrated in the right part of Fig. 3.
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 learning rate of Te for the generator (i.e., UNet) and le 4 for the discriminator. We set the momentum and / for both optimizers as 0.5 and 0.999, respectively. The total batch size is set 32 using 4

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Figure 4: Video Generation on Single Conditioning Images from Various Domains. We employ method various images generated by SDXL [60] to synthesized vidcos. The videos contain 14-frame at a resolution of 1024 x 576 with 7 FPS. The results demonstrate that our model can generate high-quality motion-consistent videos of various objects across different domains. Please refer to our webpage for whole video sequences.
steps gradient accumulation. We set the EMA rate as 0.95. We set Pmean -1, Pltd -1, and 0.1 not otherwise noted. At inference time, we sample videos at resolution 1024 x 576.
4.1 Qualitative Visualization
To comprehensively evaluate the capabilities of method, we SDXL [60] (with refiner) to generate images of different scenes at the resolution of 1024 x 1024. then perform center crop on the generated images to get resolution as 1024 576, which serves the condition of our approach to synthesize videos of 14 frames FPS. shown in Fig. 4, our method can successfully generate videos of high-quality frames and consistent object movements with only 1 step during inference.
4.2 Comparisons Results
Quantitative Comparisons. We present a comprehensive evaluation our method compared the existing state-of-the-art approach, AnimateLCM [21], UFOGen [25], LADD [28], and SVD [13]. To
7

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SVD (25 stpps B66
STD steo 1ONK
SVD @ stgas) 2.9K
Aninste... 300po 7,89
AADD (1 (ew
224954
SXD (25 stggs)
SSD (14 (16-9-pA)) S-gN)
LIME
SV 08 siggs
2.00X
66 stepa T.21
LADD sogg
22.97
UROGG (LsRg)
ZZ07IX
Ohr's
strp 22.07X
Figure 5: Comparison between SVD [13], 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 generate videos under 25, 16, and 8 sampling steps, AnimateLCM to synthesize videos under 4 sampling steps, LADD and UFOGen to generate videos under sampling step. Animatel.CM, LADD and UFOGen generates blurry frames with few-steps and single-step sampling. Our approach can accelerate the sampling speed by 22.9x compared with SVD while maintaining similar frame quality and motion consistency.

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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 AnimateLCCM authors. Additionally, we include the officially released Animatel MCM-xtl. II [21] by evaluating the first 14 generated frames and denote the approach as Animatel.CM'. We try our best to implement LADD [28] and UFOGen [25] and denote respectively as LADDÍ, 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 X 576 at 7 FPS for all methods. The generation results are then resized back to 320 x 240 for FVD calculation. Our method is compared against SVD [13] and Animatel.Co. L. [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 diffussin 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 23x speedup. Our method also outperforms the steps sampling results for AnimateLCM and AnimateLCM" indicating speedup of more than 6x. For single-step evaluation, our method performs much better than existing step-distillation methods [25, 28] built upon maag--aaeedddiffusic 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 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 AnimateL LCM [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.
4.3 Ablation Analysis
Effect of Diseriminator 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 peatial-tempord generator, the receptive field 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 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), Pmean and P Pata control the distribution of of, which is the noise level added to zo or do 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 of is concentrated on small values, e.g., Pmean = 2 and Psta = -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., Pmean 1 and Padd 1, the discriminator input, which is (in(a)) =
Buta/e results in small adversarial gradients for the Vot²+1
generator. This causes increased artifacts in the generated videos, shown in Fig. 7 and Tab. 3.

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Table 2: Analysis of discrim- Table 3: FVD vs. o' distribu-
Mi
inator. We measure FVD for tions. models with different discrim- inator configurations. "SP" in- dicates that spatial heads and "TE" indicates temporal heads.
P Pmean
Pstd
FVD
0180
-20
1.0
3370.4
-1.0
-1.0
180.9
SP+TE
SP
TE
0.0
1.0
416.7
FVD
180.9
514.7
539.2
1.0
1.0
632.9
Figure 6: PDF of
Pmeax -2, Pstd
Pmeaa
Pmovon
Paca
Pmnrn
P.P
Figure 7: Analysis of o' Distributions. We investigate the impact of changing the distribution of of by adjusting Pmean and Patd. The results are shown with the same image conditioning. The first row and the second row display the first and last frames generated, respectively.
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 broader audience by delivering more than 20x speedup for the denosing process.
(25 steps.
SVD
Ours step)
Figure 8: Limitations. We show that, for some conditional images, our model tends to generate 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, blurry frames, as shown in Fig. 8. Such artifacts introduced by the original SVD model, as 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 considerable portion of the overall runtime. We leave the acceleration of these models as future work.
10

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Acknowledgments and Disclosure of Funding
This research has been partially funded by grants to D. Metaxas from NSF: 2310966, 2235405, 2212301, 2003874, 1951890, AFOSR 23RT0630, and NIH 2R01HL127661.
References
[1] Jonathan Ho, Ajay Jain, and Pieter Abheel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:68400-851,, 2020. 2020.
[2] 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 computer vision and pattern recognition, pages 10684-10650 10695, 2022.
[3] Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Jiang, and José Lezama. Photorealistic video generation with diffusion models. arXiv preprint arXiv:2312.06662 2023.
[4] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung. Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu. Krishna Somandepalli. Hassan Akbari, Yair Alon, Yong Cheng, Joshua Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez. David Minnen, Mikhail Sirotenko, Kihyuk Sohn. Xuan Yang. Hartwig Adam, Ming-Hsul Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, and Lu Jiang. Videopoct: A large language model for zero-shor shot video generation. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net, 2024.
[5] Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov, Ekaterina Deyneka, Tsai-Shien Chen, Anil Kag, Yuwei Fang. Aleksei Stoliar, Elisa Ricci, Jian Ren, et al. Snap video: Scaled spatiotemporal transformers for text-to-vider video synthesis. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 7038-7048, 2024.
[6] Zhixing Zhang, Bichen Wu, Xiaoyan Wang, Yaqiao Luo, Luxin Zhang, Yinan Zhao, Peter Vajda, Dimitris Metaxas, and Licheng Yu. Avid: Any-length video inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 7162-7172, 2024.
[7] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farles Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
[8] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Flect. Video diffusion models. Advances in Neural Information Processing Systems, 35:8633-8644 2022.
[9] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kam- yar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurlPS 2022, New Orleans, LA. USA, November 28- December 2022, 2022.
[10] Andreas Blatmann, Robin Rombach, Huan Ling, Tim Dockhorn, Scung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution vidco synthesis with latent diffusion models. In Proceedings of the IELE/CVF Conference on Computer Vision and Puttern Recognition, pages 22563- 22575, 2023.
[11] 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. a-vido:: Text-to-video generation without text-video data, In The Eleventh International Conference on Learning Representations. ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
[12] Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Hanshu Yan. Lia Liu, Chenxu Zhang, Jiashi Feng, and Mike Zheng Shou. Magicanimate: Temporally consistent human image animation using diffusion model. arXiv preprint arXiv:2311 164988 2023.
[13] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch. Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets, arXiv preprint 2rXhv:2311.5117.222 2023.
11

---

[14] Shiwei Zhang, Jiayu Wang. Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou. 12vgen-xl: High-quality image-to- video synthesis via cascaded difffusion models. arXiv preprint wrXiv:2331.04144 2023.
[15] Zuozhuo Dai. Zhenghao Zhang, Yao Yao, Bingxue Qiu, Siyu Zhu. Long Qin, and Weizhi Wang. Ani- mateanything: Fine-grained open domain image animation with motion guidance. 2023.
[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai. Animated- iff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv.2307.04725. 2023.
[17] Jiuniu Wang. Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, and Shiwei Zhang. Modelscope text-to-vidoo technical report. arXiv preprint arXiv:2308 2362::2300006677,,, 2023.
[18] Omer Tal, Hila Chefer, Omer Tov, Charles Hermann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Yuanzhen Li, Tomer Michaeli, et al. Lumiere: A space-time diffusion model for video generation. arXiv preprint arXiv:2401.12900 2024.
[19] Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elhoseiny. Stylegan-v: continuous video generator with the price, image quality and perks of stylegan2. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 3626-3636, 2022.
[20] Han Zhang. Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 5907-5915, 2017.
[21] Fu- Yun Wang. Zhaoyang Huang, Xiaoyu Shi, Weikang Bian, Guanglu Song, Yu Liu, and Hongsheng Li. Animatelcm: Accelerating the animation of personalized diffusion models and adapters with decoupled consistency learning. arXiv preprint arXiv:2402.00770 2024.
[22] Shanchuan Lin and Xiao Yang. Animatediff Animatedifflighhning: model diffusion distillation. arXiv preprint arXiv:2403.12700 2024.
[23] Xiang Wang, Shiwei Zhang, Han Zhang, Yu Liu, Yingya Zhang, Changxin Gao, and Nong Sang. Videolem: Video latent consistency model. arXiv preprint arXiv:2312.09109, 2023.
[24] Yang Song, Prafulla Dhariwal. Mark Chen, and Ilya Sutskever. Consistency models. In Andreas Krause. Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt. Sivan Sabato, and Jonathan Scarlett, editors, International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu. Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 32211-32252. PMLR, 2023.
[25] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to- image generation via diffusion gans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196-888 8206, 2024.
[26] Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 661 6613-6623, 2024.
[27] Axel Sauer, Dominik Lorenz, Andreas Blatmmann, and Robin Rombach. Adversarial diffusion distillation. arXiv preprint arXiv:2311.1704,, 2023.
[28] Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rom- bach. Fast resoutton image synthesis with latent adversarial diftusion distillation. arXiv preprint 1XXiv24403112015 2024.
[29] Shanchuan Lin, Anran Wang, and Xiao Yang. Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv: 13929, 2024.
[30] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier. Marcin Michalski, and Sylvain Gelly, FVD: A new metric for video generation. In Deep Generative Models for Highly Structured Data, ICLR 2019 Workshop, New Orleans, Louisiana, United States, May 6, 2019. OpenReview.net, 2019.
[31] Sergey Tulyakov, Ming- Yu Liu, Xiaodong Yang. and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1526-1535, 2018.
[32] Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, and Sergey Tulyakov. A good image generator is what you need for high-resolution video synthesis. In 9th International Conference Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021.
12

---

[33] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565-26577, 2022.
[34] Robin Rombach, Andreas Blattmann. Dominik Lorenz. Patrick Esser, and Björn Ommer. sigh-resolution image synthesis with latent diffusion models. In Proceedings of the IEEF/CVF Conference Computer Vision and Pottern Recognition, pages 10684-10695, 2022.
[35] Rohit Girdhar, Mannat Singh, Andrew Brown, Quentin Duval, Samaneh Azadi, Sai Saketh Rambhatla, Akbar Shah, Xi Yin, Devi Parikh, and Ishan Misra. Emu video: Factorizing text-to-video generation by explicit image conditioning, arXiv preprint arXiv:3311.0779,, 2023.
[36] 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-vidco data, In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
[37] Songwei Ge. Seungjun Nah, Guilin Liu, Tyler Poon, Andrew Tao, Bryan Catanzaro, David Jacobs, Jia- Bin Huang, Ming-Yi Yu Liu, and Yogesh Balaji. Preserve your own correlation: A noise prior for video diffusson models. In Proceedings of the IEEE/CVF International Conference Computer Vision, pages 2023.
[38] Ruben Villegas, Mohammad Babacizadeh, Picter-Jan Kindermans, Hernan Moraldo, Han Zhang, Moham- mad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length vidco generation from open domain textual descriptions. In International Conference on Learning Representa- tions, 2022.
[39] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole. Mohammad Norouzi, David Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.0230: 2022.
[40] Xiang Wang, Shiwei Zhang, Han Zhang, Yu Liu, Yingya Zhang, Changxin Gao, and Nong Sang. Videolem: Video latent consistency model. arXiv preprint arXiv:2312.09109, 2023.
[41] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022.
[42] Yanyu Li, Huan Wang, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang. Sergey Tulyakov, and Jian Ren, Snapfusion: Text-to-image diffusion model on mobile devices within two seconds. Advances in Neural Information Processing Systems, 36, 2024.
[43] Yang Song and Prafulla Dhariwal. Improved techniques for training consistency models. arXiv preprint arXiv:2310.1411 2023.
[44] Simian Luo, Yigin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04373, 2023.
[45] Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu, Patrick von Platen, Apolinário Passos, Longbo Huang, Jian Li, and Hang Zhao. Lem-lora: A universal stable-diffusion acceleration module. arXiv preprint vrXiv:2311.05556 2023.
[46] Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang. Wu, Pan Xie, Xing Wang, and Xuefeng Xiao. Hyper-sd: Trajectory segmented consistency model for efficient image synthesis. arXiv preprint arXiv:2404. 13686, 2024.
[47] Dongjun Kim, Chich-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon. Consistency trajectory models: Learning probability flow ode trajectory of diffusion. arXiv preprint arXiv:2310.02273 2023.
[48] Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao, and Tat-Jen Cham. Trajectory consistency distillation. arXiv preprint arXiv:2402.1911 19159, 2024.
[49] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
[50] Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng. et al. Instaflow: One step is enough for high- quality diffusson based text-to-image generation. In The Tweifihh International Conference on Learning Representations, 2023. 2023.
13

---

[51] Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda. and Ali Thabet. Imagine flash: Accelerating diffusion models with backward distillation. arXiv preprint arXiv:2405.05224 2024.
[52] Yang Song. Jascha Sohl-Dickstein Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. International Conference on Learning Reprexentations, ICLR 2021, Virtual Event, Austria, May 3-7. 2021. OpenReview.nee 2021.
[53] Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, and Timo Aila. Stylegan-t: Unlocking the power of gans for fast large-sces scale text-to-iman -o-tage-maae synthesis. In International conference on machine learning, pages 30105 30 PMLR, 2023.
[54] Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger. Projected gans converge faster. Advances in Newral Information Processing Systems, 34:17480-1788 17492, 2021.
[55] Jae Hyun Lim and Jong Chul Ye. Geometric gan. arXiv preprint arXiv:1705.02080 02894, 2017.
[56] Pierre Charbonnier, Laure Féraud, Gilles Aubert, and Michel Barlaud. Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on image procesxing, 6(2):298-311, 1997.
[57] Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International conference on machine learning, pages 3481-3490. PMLR, 2018.
[58] Takeru Miyato and Masanori Koyama. cgans with projection discriminator. International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018.
[59] Rohan Anil, Vineet Gupta, Tomer Koren, and Yoram Singer. Memory efficient adaptive optimization. Advancex in Neural Information Processing Systems, 32, 2019.
[60] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arX1v:300..01952, 2023.
[61] Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu, and Jia-Bin Huang. On the content bias in fréchet video distance. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), 2024.
[62] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: dataset of 101 human actions classes from videos the wild, arXiv preprint 122.0:12 2012.
[63] Jonathan Ho and Tim Salimans. Classifier-Be free diffusion guidance. arXiv preprint arXiv:2207.15598
2022.
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Reviews:
[{"review_id": "0cqtqy0gIW", "reviewer": "Reviewer_R1mD", "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.", "questions": "- GANs suffer from mode collapse problem how was it ensured that is not the case here with this approach?\n\n- GANs suffer from the problem of instability in training how was it handled \n\n- Why weren't standard video datasets like UCF101 used in evaluation?", "limitations": "GAN based models suffers from training instability because of the presence of adversarial loss.", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The idea is sound and well founded. Utilizing GAN based distillation approach to reduce the computational overhead related to the sampling process in diffusion models. This is especially important for videos since a minute long video consists of almost 1.5k frames.", "weaknesses": "- Videos do not look like they're real world videos. How does this work with the real world videos.\n\n- The motion is very laminar in the videos. How is the performance when trained on videos with non laminar motion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.\n\n---\n\n**Q1. About generating real world videos.**\n\nA1. Thanks for the suggestions. In this work, we fine-tune SVD, which is an image-to-video model, into single sampling step. Our model can take *arbitrary* given image as input and generate the corresponded video. \n\nIn the main paper (Line 221-222 and Fig. 5), we show the results of using the real images as input to generate the real world videos. The saved videos can be found in our Supplementary Materials. Additionally, we compare our generation results with other approaches. \n\nWe also provide more examples of using real images as input to generate real world videos. The examples are shown in the Fig. A in the *attached one-page PDF*.\n\n---\n\n**Q2. About some videos with laminar motion.**\n\nA2. Thank you for noticing the laminar motion for some videos. We would like to kindly mention that this paper aims to improve the sampling efficiency of the pre-trained SVD model, instead of improving the motion quality of SVD. In fact, we have shown in the main paper (Fig. 5) and the *Comparisons section* in the webpage of the Supplementary Material, the motion quality of our approach is *similar* to the motion of 25-steps of the original SVD model.\n\nIn fact, we use training videos with large motion to fine-tune SVD. Some examples of training videos are shown in the Fig. B of the *attached one-page PDF*. Nevertheless, improving the motion of SVD without modify its whole architecture (*e.g.,* image-based encoder), is still challenging, which also beyonds the scope of this paper. We further provide more examples in Fig. C of the *attached one-page PDF*,  demonstrating that for the same conditioning image, running SVD with 25 steps can also generate videos with laminar motion.\n\nOne reason of that SVD generates videos with laminar motion is due to the SVD can only synthesize short clips. We believe that in the future, with more open sourced video generation models synthesizing minutes-long videos, the motion can be significantly improved. By that time, we would be very interested in applying our approach to those models.\n\n---\n\n**Q3. About the training challenges of using GAN, such as mode collapse and instability.**\n\nA3. We agree with the reviewer that applying GAN during training is non-trivial. To avoid the mode collapse and achieve the stabilized training, we carefully design our training pipeline. For instance, we use hinge loss during adversarial training. Additionally, we employ reconstruction objective (Eq. 9 in the main paper) and R1 gradient penalty (Eq. 11 in the main paper) during training to make the training process more stable. Moreover, based on the EDM-framework, we observe that sampling $t^\\prime$ using a discretized lognormal distribution provides more stable adversarial training.\n\nWe have also tried to re-train our model multiple times using the same dataset and approach. The training is stable across different runs and we can re-produce very similar results.\n\n---\n\n**Q4. standard video datasets like UCF101 used in evaluation**\n\nA4. We agree with the reviewer that UCF-101 is a standard dataset that should be used in the evaluation. \n\nIn the paper, we mainly use UCF-101 to obtain the quantitative results. For example, in Line 211 of main paper, we explain the details of how we use UCF-101. In Tab.1, Tab.2, and Tab.3, we show the calculated FVD on UCF-101 to compare our approach with others and ablating the design principles of our approach.", "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": "BiePfTiAVF", "reviewer": "Reviewer_Do55", "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.\nTo 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.\n\nThe results are of slight better quality than 16 steps of SVD, and slightly worse than 25 steps of SVD.", "questions": "- L257: what's the relative compute / time that goes into the encoder / decoder compared to the actual latent model?", "limitations": "The authors briefly discuss the fact that the VAE decoder & encoder for image conditioning.\n\nIt should be noted that training GANs can often come with the additional headache of instabilities.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is very clear and easy to read\n- The presented approach is simple\n- It works! Quality is somewhat comparable to several diffusion steps with the original model", "weaknesses": "- 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\n- The proposed method is fairly simple and incremental, though the results are better than existing methods", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.\n\n---\n\n**Q1. About improving the writing for the preliminary section.**\n\nA1. Thanks for the suggestion! We will revise the writing of the manuscript.\n\n---\n\n**Q2. About the approach is simple.**\n\nA2. We agree with the reviewer that our approach, built upon adversarial training, is relative straightforward and simple to re-implement. On the other hand, as high-lighted by the reviewer, our approach actually works, which we believe further demonstrate the value of this paper - simple yet effective. In fact, our approach also outperforms other distillation based methods like latent consistency model.\n\nAdditionally, this is the first paper showing that a single forward video diffusion model can obtain similar quality as a 25-step model. Such results and findings could inspire later research and efforts to develop more advanced approaches to further improve the generation quality.\n\n---\n\n**Q3. Inference time for image encoding and encoding.**\n\nA3. We calculate sampling time for each component when generating $14$-frames videos at a resolution of $1024\\times 576$. We use A100 GPU as the testbed to get the latency.\n\n- SVD use both CLIP image encoder and VAE encoder to encode the first frame of the video as the condition input:\n    - The runtime for CLIP image encoding is 0.03 s.\n    - The runtime for VAE image encoding is 0.01 s.\n- The latency for forwarding the UNet once is 0.30 s.\n- The latency for VAE decoding the latent to get videos is 2.32 s.\n\n---\n\n**Q4. Training instabilities of GANs.**\n\nA4. We agree with the reviewer that applying GAN during training is non-trivial. To avoid the mode collapse and achieve the stabilized training, we carefully design our training pipeline. For instance, we use hinge loss as the adversarial training. Additionally, we employ reconstruction objective (Eq. 9 in the main paper) and R1 gradient penalty (Eq. 11 in the main paper) during training to make the training process more stable. Moreover, based on the EDM-framework, we observe that sampling $t^\\prime$ using a discretized lognormal distribution provides more stable adversarial training.\n\nWe have also tried to re-train our model multiple times using the same dataset and approach. The training is stable across different runs and we can re-produce very similar 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": "DJ1BQZ9Bix", "reviewer": "Reviewer_FT7s", "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.", "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 are briefly addressed but not sufficiently analyzed. It would be great to see examples of errors introduced by the approach or some further analysis.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "The paper is very well written and easy to read. In particular, the introduction and related work well put the work in context and the contribution. The related work section is sufficiently extensive and provides the right context to identify the existing gap the paper is trying to solve. The results and visualizations are clear, well-explained, and detailed. \n\nThe paper is also well validated, showing excellent results compared to the state-of-the-art in single-step video generation, achieving superiority in terms of quality as well as sampling time. \n\nBy fine-tuning an existing video model, the authors can also achieve good training time and use any knowledge captured by the pretrained model. In that sense, the proposed approach makes sense and is well-suited for the task.", "weaknesses": "My main concerns are concerning the novelty and significance of the proposed approach in the following sense:\n\n1. 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. \n\n2. 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. \n\nFurther, 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comments. We provide detailed response in the following.\n\n---\n\n**Q1. About novelty.**\n\nA1. We agree with the reviewer that certain design components, *e.g.,* adversarial training and constructing the discriminator initialized from the diffusion model, have been explored in different manners in other tasks. However, we would also like to kindly emphasize that distilling a video diffusion model into single-step with the similar performance as 25-steps *has not been studied* in the literature.\n\nIn the following, we would like to kindly clarify that the novelty comes from the design of the whole training pipeline that is tailored for solving a specific challenging task, instead of the individual component like adversarial training.\n\nOur framework is inspired by LADD [27], which distills an image diffusion model into fewer steps. Here, we highlight major differences compared with LADD.\n\n- _First_, we extend the image latent adversarial distillation from image to the video domain by incorporating spatial and temporal heads to achieve one-step generation for video diffusion models. The training computation to distill a video model is much larger than distilling an image model. Therefore, we carefully design our discriminator, which is not used in the image domain, to enable the stable training. Notably, our discriminator only uses certain part of the UNet that we find important. Such design is different from exiting arts that re-use the whole UNet. 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. \n\n- _Second_, based on the EDM-framework, we observe that sampling $t^\\prime$ using a discretized lognormal distribution provides more stable adversarial training compared to the logit-normal distribution used in LADD. \n\n- _Third_, unlike LADD, we utilize real video data instead of synthetic data for training, removing the storage and computation burden for calculating and saving the synthetic data. \n\n- *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.\n\nIn we only focus on how we design the discriminator (*e.g.,* re-using some weights from the generator and fixing them), indeed, they do not look fancy and seem like lacking novelty. However, we humbly think it is important to look at how such design, when smoothly combined with techniques (like how to sampling the noise), can solve an important task.\n\nWe hope such an explanation would make sense to the reviewer.\n\n---\n\n**Q2. Comparison with the approaches that built for distilling image diffusion models [24, 25, 26, 27, 28].**\n\nA2. Directly taking existing step-distillation methods for distilling text-to-image models to video generation is not sufficient to achieve reasonable results. As suggested by the reviewer, we compare our approach with the image-based distillation methods.\n\n- First, DMD [25] and ADD [26] are too computationally costly to implement in video generation tasks. Both of these works require loss calculation in the pixel space instead of latent space. Forwarding and backwarding through the VAE decoder while training will introduce a huge GPU memory overhead and slow down the whole training process.\n\n- Second, since LADD [27] outperforms SDXL-Lightning [28] by significant margin [27], we try our best to implement LADD [27]. We also implement UFOGen [24].  We replace the generator in LADD and UFOGen with SVD. Please kindly notice that simply re-using the discriminator from LADD and UFOGen leads to the *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 and UFOGen to the one proposed in our work.\n\nWe report the FVD score on UCF-101 in the following table. Additionally, we provide qualitatively comparisons between our method and  LADD and UFOGen in the Fig. C of the *attached one-page-PDF*. \n\n>| Method | UFOGen | LADD | Ours |\n| --- | --- | --- | --- |\n| FVD | 1917.2 | 1893.8 | 180.9 |\n\nFrom these results, we can see that, for distilling video diffusion models, our approach performs much better than exiting step-distillation methods built upon image-based-diffusion models.\n\n---\n\n**Q3. Importance of the proposed training regime**\n\nA3. We are not sure whether we fully understand this concern, yet, please allow us to try to answer them in three different perspectives.\n\n- First, as the experiments shown in A2, our adversarial training regime is very important, as it achieves better results than the ones proposed in LADD [27] and UFOGen [24], which are two methods for distilling image-based-diffusion models.\n\n- Second, the reviewers suggest apply our approach for two work: SVD[14] and AnimateLCM [21]. Actually, our video model is fine-tuned from SVD [14]. In Tab.1 of the main paper, we show single-step model achieves similar results as 25-steps SVD, and better results than 8-steps AnimateLCM [21].  \n\n- Third, directly applying our training pipeline to the image-based-diffusion model is non-trivial. For example, It requires the changing of our discriminator, the noise sampling process, and the changes of adversarial losses. We humbly think such experiments are beyond the scope of this work.\n\n---\n\n**Q4. Discussion about limitations**\n\nA4. Thank you for the suggestion. 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 the Fig. D of the *attached one-page PDF*. Such artifacts are introduced by the original SVD model,  as can be observed in Fig. D of the *attached one-page PDF*. We believe a better text-to-video model can solve such issue and are interested in trying our step-distillation approach to such a model when there is an open-sourced one.", "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": "fpkeqieNWE", "reviewer": "Reviewer_Q9Lc", "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.", "questions": "see Weaknesses", "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.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The algorithm proposed by the authors significantly improves video generation inference speed without compromising the quality of the generated results.\n2. The authors propose using two parallel heads, a spatial head and a temporal head, to implement the discriminator. This resolves the issue of a single head potentially leading to the generation of static videos.", "weaknesses": "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?\n2. 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?\n3. 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?\n4. 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive comments. The detailed responses regarding each concern are listed below.\n\n---\n\n**Q1. Reproducing the results.**\n\nA1. We plan to make our code and checkpoint public for the reproducing of our work.\n\n---\n\n**Q2. Training data.**\n\nA2. Our dataset contains around 1M video clips of various lengths. As we mentioned in the paper (Line 191-192), our model is trained for 50K iterations with a total batch size of 32. For each training sample, we randomly select 14 consecutive frames for a video temporally downsampled to 7 FPS. We agree our dataset is far less the one used for SVD training. However, in this work, we fine-tune SVD for efficient generation instead of learning motion from scratch, which makes our model requires less data. We humbly believe it is an advantage of our approach that we can require much less data than SVD while being able to fine-tune the model to single-step.\n\nOn the other hand, we do expect our model can benefit from more high-quality data training and longer training iterations. To validate the assumption, we run the following experiment. \n\n- First, we randomly selected 700K videos out of the 1M dataset and use that to train our model.  Our method achieves FVD of $275.7$ with 700K videos while $180.9$ using 1M videos. We also provide the qualitative results in Fig. C of the *attached one-page PDF*. \n\n- Second, we report the performance of the model under various training iterations. Due to the limit of computation, we report results for training the model within 50K iterations. In the following table, we show how the FVD score changes along the training process.\n\n>| Iters (K) | 20 | 30 | 40 | 50 |\n| --- | --- | --- | --- | --- |\n| FVD | 542.2 | 479.5 | 217.1 | 180.9 |\n\n---\n\n**Q3. Discriminator initialization.**\n\nA3. We use the encoder of the UNet to initialize the backbone for its strong video understanding ability and we fix the weights of the backbone during training. Such design can further facilitate faster training and fewer memory consumption.\n\nTo further demonstrate the effectiveness, we train the discriminator backbone together with the added heads *without* initializing it from the pre-trained UNet encoder. We observe a significant drop of performance, *i.e.,* the FVD on UCF-101 is 1490.2 (without initialization from UNet) vs. 180.9 (with initialization of UNet.). We also provide qualitative results in Fig. C of the *attached one-page-PDF*. \n\n---\n\n**Q4. Distillation loss.**\n\nA4. There are two main reasons for not using the teacher-student distillation loss in ADD [26]. \n\n- First, to obtain distillation target in ADD [26], during training, the denoising process for the teacher model is required. Thus, the teacher model needs to run multiple inference steps. Such a training process can drastically increase the training time. \n\n- Second, distillation loss in ADD [26] needs to be calculated in the pixel space instead of latent space. Decoding the latents and calculating the gradients with the video VAE decoder will introduce significant computation overhead for video models, leading to out-of-memory issue for SVD.  Thus, we use the reconstruction objective instead of the distillation loss in our approach to make the model training feasible. \n\n---\n\n**Q5. Generalization ability.**\n\nA5. As shown in the Supplementary Materials, we qualitatively evaluate our method on images of various styles and objects, demonstrating the strong generalization ability of our method for video generation. \n\nAs for the quantitative evaluation metric, we chose FVD on UCF-101 as it is the most adopted benchmark for video generation. For example, it is adopted in SVD[14] and AnimateLCM [21]. We fully agree with the reviewer that more benchmark datasets are helpful for video model evaluation and it is an exciting direction for future work.", "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 sincerely thank all reviewers for their thoughtful comments and positive feedbacks. We appreciate their findings of the strengths for this paper, including:

- **our studied problem** (reducing the computational overhead for video diffusion models) is important (Reviewer R1mD) and well validated (Reviewer FT7s); 

- **our approach and idea** are sound and well founded (R1mD), are simple and working (Reviewer Do55), make sense and well-suited for the studied task (Reviewer FT7s), and resolve the issue of generating static videos (Reviewer Q9Lc);

- **our results** are excellent, clear, well-explained, and detailed (Reviewer FT7s), are comparable to original model with several diffusion steps (Reviewer Do55), achieve SOTA SVD quality and sampling time in 1-step (Reviewer FT7s), and significantly improve video generation inference speed (Reviewer Q9Lc);

- **our paper** is very clear, well-written, and easy to read (Reviewer Do55, FT7s) with sufficient related work and the right context (Reviewer FT7s).

In the following, we provide detailed answer to the major concern of each reviewer. Additionally, we attach an **one-page PDF** with more visualization results.

Meta-review: 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.
Decision: Accept (poster)