Title: SF-V: Single Forward Video Generation Model
Venue: NeurIPS 2024
Accepted: yes
Keywords: Video generation

diffusion model

distillation

adversarial training
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

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 pretrained 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 × speedup compared with SVD and 6 × speedup compared with existing works, with even better generation quality), paving the way for real-time video synthesis and editing.

1 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.

2 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.

3 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.

3.1 Preliminaries of Stable Video Diffusion

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:

where 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:

where F θ is the network to be trained. The preconditioning functions are set as c skip ( σ ) = ( σ 2 +1) -1 , c out ( σ ) = -σ √ σ 2 +1 , c in ( σ ) = 1 √ σ 2 +1 , and c noise ( σ ) = 0 . 25 log σ .

Generator

Discriminator

Pre-trained Weights

Ge

D

米

Spatial Heads

Temporal Heads

不

Fake

Real

Ct

Ladu

V

Ot

diffuse

diffuse

diffuseot

Stable Video Diffusion. The 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:

where 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]:

where σ t is obtained with where T is the total number of denoising steps and ρ is a hyper-parameter controlling the emphasis level to low noise levels.

3.2 Latent Adversarial Training for Video Diffusion Model

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.

Latent Adversarial Training. We use a pair of generated samples ˆ x 0 and real samples x 0 to conduct the adversarial training. Specifically, during training, the generator G θ produces generated samples ˆ x 0 ( 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 T g -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 ˆ x 0 is given by:

To train the discriminator, we forward the generated samples ˆ x 0 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 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. (5), according to a discretized lognormal distribution defined as:

where 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 ˆ x ′ t = ˆ x 0 + σ ′ t ϵ and x ′ t = x 0 + σ ′ t ϵ , respectively.

Following literature [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 (ˆ x 0 , ϕ ) is defined as:

Furthermore, we notice that a reconstruction objective, L recon , between x 0 and ˆ x 0 can significantly improve the stability of the training process. We use Pseudo-Huber metric [56, 43] for reconstruction loss, as:

where c > 0 is an adjustable constant. Thus, the overall objective for training the generator is as follows with λ balances two losses:

Other other hand, the discriminator is trained to minimize:

where R 1 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.

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 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.

4 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 50 Kiterations on 8 NVIDIA A100 GPUs, using the SM3 optimizer [59] with a learning rate of 1 e -5 for the generator ( i.e. , UNet) and 1 e -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

4K KOXSEC2

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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 .

4.1 Qualitative Visualization

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.

4.2 Comparisons Results

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

SVD

(25 steps)

0.64

X

SVD

(16 steps)

1.00

X

SVD

(8 steps)

2.00

X

AnimateLCM

(4 steps)

7.49

X

Ours

(1 step)

22.97

X

SVD

(25 steps)

0.64

X

SVD

(16 steps)

1.00

X

SVD

(8 steps)

2.00

X

AnimateLCM

(4 steps)

7.49

X

LADD

(1 step)

22.97

X

Ours

(1 step)

22.97

X

UFOGen

(1 step)

22.97

X

LADD

(1 step)

22.97

X

UFOGen

(1 step)

22.97

X

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 8 -steps 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.

4.3 Ablation Analysis

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 ˆ x 0 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 and P std = 1 , the discriminator input, which is c in ( σ ′ t )ˆ x ′ t = ˆ x 0 + 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.

Table 2:

Analysis of discrim-

SP+TE

SP

TE

FVD

180.9

514.7

539.2

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416.7

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5 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.

SVD

(25 steps)

Ours

(1 step)

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 V AE 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.

0

.

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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.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n1. 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.\n\nWEAKNESSES:\n1. 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?\n\nQUESTIONS:\nsee Weaknesses\n\nLIMITATIONS:\nIt'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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\nThe 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.\n\nWEAKNESSES:\nMy 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?\n\nQUESTIONS:\nI 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.\n\nLIMITATIONS:\nLimitations are briefly addressed but not sufficiently analyzed. It would be great to see examples of errors introduced by the approach or some further analysis.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

comments: 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.

STRENGTHS:
- The paper is very clear and easy to read
- The presented approach is simple
- 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
- The proposed method is fairly simple and incremental, though the results are better than existing methods

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.

It should be noted that training GANs can often come with the additional headache of instabilities.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-12

comments: 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.

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.

- The motion is very laminar in the videos. How is the performance when trained on videos with non laminar motion.

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:
GAN based models suffers from training instability because of the presence of adversarial loss.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-23