Diffusion Adversarial Post-Training for One-Step Video Generation
Authors: Shanchuan Lin
Xin Xia
Yuxi Ren
Ceyuan Yang
Xuefeng Xiao
Lu Jiang
Venue: ICML 2025 poster (2025)
Keywords: video generation
image generation
diffusion
adversarial training
GAN
real-time
fast
distillation
generative model
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: Convert a slow diffusion model into a one-step, real-time, high-resolution video generator through adversarial post-training

Abstract: The diffusion models are widely used for image and video generation, but their iterative generation process is slow and expansive. While existing distillation approaches have demonstrated the potential for one-step generation in the image domain, they still suffer from significant quality degradation. In this work, we propose Adversarial Post-Training (APT) against real data following diffusion pre-training for one-step video generation. To improve the training stability and quality, we introduce several improvements to the model architecture and training procedures, along with an approximated R1 regularization objective. Empirically, our experiments show that our adversarial post-trained model can generate two-second, 1280x720, 24fps videos in real-time using a single forward evaluation step. Additionally, our model is capable of generating 1024px images in a single step, achieving quality comparable to state-of-the-art methods.

Diffusion Adversarial Post- -rainTrain for One-Step Video Generation
Shanchuan Lin Xin Xia
Yuxi Ren"
Ceyuang Yang' Xuefeng Xiao
Lu Jiang'
Abstract
challenges. It deviates from the fundamental principle of diffusion models, which rely on iterative denoising steps to uncover the data distribution. While previous research has demonstrated notable advancements for generating images in a single step with promising results (Ren et al., 2024; Yin et al., 2024a; Sauer et al., 2025; Lin et al., 2024), producing high-quality images in one step remains challenging, partic- ularly in achieving fine- fine-grained details, minimizing artifacts, and preserving the structural integrity.
The diffusion models are widely used for image and video generation, but their iterative genera- tion process is slow and expansive. While ex- isting distillation approaches have demonstrated the potential for one-step generation in the image domain, they still suffer from significant quality degradation. In this work, we propose Adver- sarial Post- fast-Training (APT) against real data fol- lowing diffusion pre-training for one-step video generation. To improve the training stability and quality, we introduce several improvements to the model architecture and training procedures, along with approximated R1 regularization ob- jective. Empirically, our experiments show that our adversarial post-trained model can generate two-second, 1280x720, 24fps videos in real- using single forward evaluation step. Addition- ally, our model is capable of generating 1024px images in single step, achieving quality com- parable to state-of of-the-rrr methods. Our project page: htpp:///eaweee-apt..ommm
Accelerated video generation, however, has seen limited progress in the literature. Early efforts utilizing generative adversarial networks (GANs) (Goodfellow et al., 2014), e.g., StyleGAN-V (Skorokhodov et al., 2021) can only generate domain-specifir specific data with poor quality in modern standards. With the rise of diffusion methods, recent studies have be- gun exploring the extension of image distillation techniques to video diffusion models. However, existing works have only explored distillation on small-scale and low-resolution video models that only generate 512 videos for a total of 16 frames (Lin & Yang, 2024; Zhai et al., 2024). A con- current work (Yin et al., 2024b) has attempted distillation of large-scale video models at 640x352 12fps. These methods still generally need 4 diffusion steps. Given the prohibitive computational cost associated with high-resolution video generation, e.g., generating just a few seconds of 1280 720 24fps videos can take multiple minutes even on the state- of-the-art GPUs like the H100, our work aims at generating high-resolution videos in single step.
1. Introduction
The diffusion model (Ho et al., 2020; Song et al., 2020) has become the de facto method for large-scale image and video generation. Reducing the generation cost is an important research area. Among the various methods, diffusion step distillation has emerged as an effective approach reduce the inference cost. Generally, these methods start with a pre- trained diffusion model teacher that generates targets through multiple diffusion inference steps. They then apply knowledge distillation (Hinton, 2015) to train student model that can replicate the teacher's output using much fewer diffusion inference steps.
In this paper, we introduce a new approach for one-step image and video generation. Our method utilizes a pre- trained diffusion model, specifically the diffusion trans- former (DiT) (Peebles & Xie, 2023), as initialization, and continues training the DiT using the adversarial training objective against real data. It is important to notice the contrast to existing diffusion distillation methods, which use a pre-trained diffusion model as distillation teacher to generate the target. Instead, our method performs ad- versarial training of the DiT directly on real data, using the pre-trained diffusion model only for initialization. We term this method Adversarial Post-Training or APT, as it parallels supervised fine-tuning commonly performed dur- ing the post-training stage. Empirically, we observe that APT provides two benefits. First, APT eliminates the sub- stantial cost associated with pre-computing video samples
One-step generation is often considered the pinnacle of dif- fusion step distillation, yet it presents the most significant
BytteDance Seed. Correspondence to: Shanchuan Lin <peter- @ betteddane.com>>>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Diffusson Adversarial Post-Training for One-Step Video Generation
from the diffusion teacher. Second, unlike diffusion distil- lation, where the quality is inherently constrained by the diffusion teacher, APT demonstrates the ability to surpass the teacher by a large margin in some evaluation criteria, in particular, improving realism, resolving exposure issues, and enhancing fine details.
On the other hand, distributional methods only aim ap- proximate the same distribution of the diffusion teacher. Most existing methods are researched for image generation. They have severe artifacts for one-step generation and still require multiple steps to obtain desirable results. Notably, LADD (Sauer et al., 2024) and others (Kang et al., 2024; Luo et al., 2024b; Chen et al., 2024) use pre- pre-generated teacher images as the adversarial target. Lightning (Lin et al., 2024) and others (Ren et al., 2024; Kohler et al., 2024; Wang et al., 2024a) learn the teacher trajectory with the adversarial objective in the loop. DMD (Yin et al., 2023) and others (Luo et al., 2024a) apply score distillation (Wang et al., 2023b) from the teacher model. The above methods set the teacher as the upper bound for quality. DMD2 (Yin et al., 2024a) and others (Sauer et al., 2025; Chadebec et al.. 2024) apply both adversarial against real data and score distillation from the teacher model. The closest to our work is UFO-Gen (Xu et al., 2023b) which also only applies adversarial training on real data. However, its discrimina- tor adopts the DiffusionGAN (Wang et al., 2022) approach which takes the corrupted data as input. Our method feeds the discriminator with real, uncorrupted data. Hence, our ap- proach follows the standard adversarial training as in GAN more closely. Additionally, UFO-Gen's image generator and discriminator are convolutional models under 1B parame- ters, while ours are transformer models with 8B parameters and generate both image and video.
Direct adversarial training on diffusion models is highly un- stable and prone to collapse, particularly in our case, where both the generator and discriminator are large transformer models containing billions of parameters. To tackle this issue, our method introduces several key designs to stabilize training. It incorporates a generator initialized through deter- ministic distillation and introduces several enhancements to the discriminator, including transformer-basee architectural changes, a discriminator ensemble across timesteps, and an approximated R1 regularization loss to stabilize training.
By virtue of APT, we have trained what may be one of the largest GAN ever reported to date (~~16), capable of gener- ating both images and videos with a single forward evalua- tion. Our experiments demonstrate that our model achieves overall performance comparable to state-of-ther ofrt one-step image generation methods. More importantly, the best of our knowledge, our model is the first to demonstrate high-resolution video generation in a single step (1280x720 24fps), surpassing the previous ofate-of-the-as which gen- erates 512 x52 or 640x352 up to 12fps videos in four steps. On a H100 GPU, our model can generate a two- second 1 1280 720 24fps video latent using single step in two seconds. On 8 HHH000 GPUs with parallelization, the entire pipeline with text encoder and latent decoder runs in real-time.
One-Step Video Generation. One-step video generation works may trace back to the use of generative adversarial networks (Skorokhodov et al., 2021; Clark et al., 2019; Tian et al., 2021; Yu et al., 2022b). They can generate up to 1024px resolution videos but are trained only on domain- restricted data, e.g., talking head videos, and the quality is poor by modern standards. More recently, some distilla- tion works (Lin & Yang, 2024; Wang et al., 2024b; Zhai et al., 2024) have attempted to distill small-scale and low- resolution video models, i.e., AnimateDiff (Guo et al., 2024) and ModelScope (Wang et al., 2023a). These models only generate low-resolution 256px or 512px videos of 16 frames and have substantial quality degradation for one-step gener- ation. For image-to-vided generation (I2V), SF-V (Zhang et al., 2024) and OSV (Mao al., 2024) explore one-step generation of 1024x 14 frames videos. For text-to-video (T2V) generation, a concurrent work (Yin et al., 2024b) has demonstrated the generation of 640x352 12fps videos in four steps. To the best of our knowledge, our work is the first to demonstrate one-step T2V generation of x280x720 24fps videos with a duration of seconds (49 frames).
2. Related Works
Accelerating Diffusion Models. Diffusion step distilla- tion is a common and effective approach to accelerate diffu- sion models. Existing methods address this problem using either deterministic distributional methods.
Deterministic methods exploit the fact that diffusion models learn a deterministic probability flow with exact noise-to- sample mappings and aim to predict the exact teacher output using fewer steps. Prior works include progressive distilla- tion (Salimans & Ho, 2022), consistency distillation (Song et al., 2023; Song & Dhariwal, 2024; Lu & Song, 2024; Luo et al., 2023a;b), and rectified flow (Liu et al., 2023a;b; Yan et al., 2024). Deterministic methods are easy to train using simple regression loss, but the results of few-step gen- eration are very blurry, due to optimization inaccuracy and reduced Lipschitz constant in the student model (Lin et al., 2024). For large-scale text-to-image generation, determinis- tic methods generally require more steps, e.g., eight steps, to generate desirable samples (Luo et al., 2023a;b).
Stable Adversarial Training. R1 regularization (Roth et al., 2017) has been shown effective for GAN convergence (Mescheder et al., 2018). has been used by many prior GANs to improve performance (Karras et al., 2018; 2019; 2021; Kang et al., 2023; Brock et al., 2019; Huang et al.,
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Diffusion Adversarial Post-Training for One-Step Video Generation
2024a). However, many recent distillation works have ei- ther completely used R1 or only used it for parts of the discriminator network. This is likely due to its higher-order gradient computation is computationally expensive and is not supported by modern deep learning software stacks, i.e., FSDP (Zhao et al., 2023), gradient checkpointing (Chen et al., 2016), and FlashAttention (Dao et al., 2022; Dao, 2023; Shah et al., 2024). Our paper proposes an approxima- tion method address this issue and we find our approxi- mated RI loss is critical for preventing training collapse.
Leverfom Norm
Droas Alanntinn
MLP
Alantion
Iiver
Narm
3. Method
Generator Transformer (36 Layers)
Disch minator Transformer (34 Layenn)
Our objective is to convert text-to-video diffusion model to one-step generator. We achieve this by fine-tuning the diffusion model with the generative adversarial objective against real data. We refer to this process as Adversarial Post-Traine Tost-Trannng or APT, due to its resemblance to supervised fine-tuning in the conventional post-training stage.
Attentor
SMithomm
Intaaieed from consistency model
Inttal and from onmodel
Prw
Pre Hilbhoon
Erbb
Noise
Text
Textl
Figure 1: Architecture overview. Both the generator and the discriminator backbone share the diffusion transformer architecture (blue). We add additional output heads on the discriminator network to produce the scalar logit (green).
3.1. Overview
We build our method on a pre-trained text-to-video diffusion model (Seawead et al.. 2025) capable of generating both images and videos through 7 diffusion steps. The training follows adversarial optimization that alternates through a min-max game. The discriminator D classifies real samples from generated ones, maximizing DL. while the genera- tor G aims generate samples that fool the discriminator, minimizing LG. Formerly, have:
36 layers of transformer blocks, amounting to total of 8 billion parameters.
3.2. Generator
We find direct adversarial training the diffusion model leads to collapse. To tackle this, we first employ consistency distillation (Song et al., 2023; Song & Dhariwal, 2024) with mean squared error loss. The model is distilled with a constant classifier-fres guidance (Ho & Salimans, 2021) scale of 7.5 and fixed negative prompt.
LD w.o-T
[fD(((z)))
+ E ()) (1) xn
Lo E. [go(D(C(2..).))), o
Let & denote the distilled model. Given noise sample z and text condition c. the model & predicts the velocity field é, which can be converted to sample prediction é:
where N denotes the standard Gaussian distribution, and T represents the training data comprising a paired latent sample x and text condition c. The latent and noise sam- ples are of size x, = E I xk' 'w' xe' where t', h², w', e' represent the dimensions of time, height, width, and channel. The functions fD. fG, and gG are the output functions. Here, ant (Goodfellow et al., 2014): fn(x)= = 9a(x) = log a (x: and fa(x) = log(1 - (z)), where (zx) is the sigmoid function.
è G(z,c.T). a = = 6.
(3)
(4)
Although the generated sample & is very blurry, & pro- vides an effective initialization for the subsequent adversar- ial training. Therefore, we initialize our generator G with the weights of G, defined as:
the simple non-saturating vari-
Figure illustrates the overall architecture. Both the gen- erator and the discriminator backbone use the diffusion model architecture but are initialized with different strate- gies which will be discussed later in this section. Concretely, our diffusion model uses the MMDiT architecture (Esser et al., 2024) and is trained with the flow-matching objective (Lipman et al., 2023) over a mixture of images and videos at their native resolutions (Dehghani et al., 2024) in the latent space (Rombach et al., 2021). The model comprises
G(z, c) = G
(5)
For the subsequent training, we primarily focus on one-step generation capability and always feed the final timestep T to the underlying model.
3.3. Discriminator
The discriminator trained to produce a logit that effec- tively distinguishes between real samples x and generated
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Diffusson Adversarial Post-Training for One-Step Video Generation
samples é. In this subsection, we discuss several effective designs that contribute to stable training and quality im- provement. Refer to the detailed results presented in our ablation studies.
is an effective technique in facilitating the convergence of adversarial training. It penalizes the discriminator gradient Ve real data æ, preventing the adversarial training from deviating from the Nash-equilibrimm:
First, following prior works (Lin et al., 2024; Lin & Yang, 2024; Xu et al., 2023a; Sauer et al., 2024), we initialize the discriminator backbone using the pre-trained trained diffusion network and let it operate directly in the latent space. There- fore, the discriminator backbone also comprises 36 layers of transformer blocks and 8 billion parameters. We find that training all parameters without freezing improves the quality. Additionally, we find that initializing it with the original diffusion model weights, as opposed to the distilled model weights used by the generator, yields better results.
Cm = D(x, e)13
(8)
Training with RI requires higher-order gradient computa- tion. The first backward computes the discriminator gradient on input va as the R1 loss. The second backward computes the gradient of the RI loss regarding the discriminator pa- rameter for the discriminator updates. However, PyTorch FSDP (Zhao et al., 2023), gradient checkpointing (Chen et al., 20,6), FlashAttention (Dao et al., 2022; Dao, 2023; Shah et al., 2024), and other fused operators (Nvidia-Apex) do not support higher-order gradient computation or double backward at the time of writing, preventing the use of RI in large-scale transformer models.
Second, we modify the diffusion transformer architecture to produce logits. Specifically, introduce new cross- attention-only transformer blocks at the 16th, 26th, and 36th layers of the transformer backbone. Each block uses a single learnable token as the query to cross-attens to all the visual tokens from the backbone as the key and value, producing a single token output. These tokens are then channel-concatenntedd, normalized, and projected yield single scalar logit output. We find that using features from multiple layers enhances the structure and composition of the generated samples.
We propose an approximated R1 loss, written as:
LarI = | c)
((V(a,,o).oo (D(/:.))DD((aa.)))....
(9)
Specifically, we perturb the real data with Gaussian noise of small variance ar. The loss encourages the discriminator's predictions to be close between the real data and its pertur- bation, thereby reducing the discriminator gradient on real data and achieving consistent objective as the original RI regularization. Thus, the final discriminator loss LD is:
Third, we directly provide the discriminator the raw sample a, å without any noise corruptions. This avoids the introduc- tion of artifacts to our generated samples. However, since our discriminator backbone is initialized from the diffusion model, and the diffusion pre-training objective at = is not meaningful, we find using = 0 for our discriminator leads to collapse. Therefore, we propose to use an ensemble of different timestep values as input. Specifically, let ń denote the underlying discriminator model, we define the D(x,c) e) in Equation (2) as:
LD= = E [fD((( e))] + E [fa(D(G(2,c). 2conT
e) - (( X E nent
oI), e)]]
(10)
In our experiments, we use À = 100, a = 0.01 for images and a = 0.1 for videos. The approximated RI is applied on every discriminator step.
3.5. Training Details
D(x,c) (=
E
([(x,,,c]]
(6)
We first train the model on only 1024px images. We use 128~256 H100 GPUs with a batch size of 9062. The leam- ing rate is 5e-6 for both the generator and the discriminator. We find the model adapts quickly. We use an Exponential Moving Average (EMA) decay rate of 0.995 and adopt the EMA checkpoint after 350 updates on the generator before the quality starts to degrade.
where / is sampled uniformly from the interval [0, T] and then shifted by transformation function:
S e
shift(t, s) == I+ ()
(7)
The shifting factor is hyperparameter determined by the latent dimension t", N², and w'. We use s = 1 for images and s = 12 for videos for our experiment. For efficiency, we sample a single per training sample æ to compute Equation (6).
We then train the model on only 1280x 24fps videos. The videos are clipped to 2 seconds. The generator is initial- ized from the image EMA checkpoint. The discriminator re-initialized from the diffusion weights. We use 1024 H100 GPUs with gradient accumulation to reach batch size of 2048. We lower the learning rate to 3e-6 for stability and train it for 300 updates. We find that the one-step model can also perform zero-shot two-step inference with improved details, but more steps lead to artifacts.
3.4. Regularized Discriminator
Our discriminator, comprising billions of parameters, is prone to collapse. Ensuring stable training is therefore cru- cial to our problem. The RI regularization (Roth et al., 2017)
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Diffusion Adversarial Post- cost-Training for One-Step Video Generation
Difffusion
APT
Diffusion
APT
structural integrity.
For video generation, Figure 4 compares our APT one-step and two-step results with the original diffusion's 25-step results. Both the good and the bad cases of the APT method are displayed. For good cases, APT improves details and realism. The one- or two-step APT models still perform worse in terms of structural integrity and text aligmment compared 25-step diffusion. Videos are in supplementary materials.
Our 1-step APT model can sometimes generate more realistic tones and better details compared 25-step diffusion with CFG.
Figure 2:
RMSProp optimizer is used with a = 0.9, which is equiva-
4.2. User Study
lent Adam (Kingma & Ba, 2014) with 91 0, _2 = 0.9 with reduced memory consumption. We do not use weight decay or gradient clipping. The entire training is conducted in BF16 mixed precision. We use the same datasets as the original diffusion model.
Evaluation Protocol. We conduct series of user stud- ies with respect three criteria: visual fidelity, structural integrity, and text alignment. Specifically, visual fidelity accounts for texture, details, color, exposure, and realism; structural integrity focuses on the structural correctness of the objects and body parts; text alignment measures close- ness to the conditional prompts. Human raters are shown pairs of samples generated by different models and asked to choose their preferences regarding each criterion to indicate preference if a decision cannot be made.
4. Experimental Results
This section empirically verifies the proposed Adversarial Post-Traiie Dast-Trannin (APT) method. Section 4.1 provides qualita- tive comparison of our method against other one-step image generation baselines and an analysis of the characteristics of the results generated by our approach. Section 4.2 presents several user studies that quantitatively assess our method. More results are supplementary materials.
Afterward, the preference score is calculated as (G / + S + B), where G denotes the number of good samples preferred, denotes the number of bad samples not preferred, and denotes the number of similar samples without preference. Thus, a score of 0% represents equal preference between the two models. +100% represents the model is preferred over all evaluated samples, and vice versa for 10--000 100%.
Baseline. For comparison with one-step image genera- tion methods, we select
(FLUU-Schhell), SD3.5-Turbo (Sauer et al., 2024), SDXL-DMD2 (Yin et al., 2024a), SDXL-Hyper (Ren et al., 2024), SDXL-Lightning (Lin et al., 2024), SDXLL-Nitro (Chen et al., 2024), and SDXL-Turbo (Sauer et al., 2025) as the comparison base- lines. These models are selected because they are either the latest research publications or commonly available open- source distilled models. We also compare their original diffusion models, i.e., FLUX (FLUX), SD3 (Esser et al., 2024), and SDXL (Podell et al., 2023), against ours in 25 Euler steps. We use the default classifier-free guidance (CFG) (Ho & Salimans, 2021) setting of each model as con- figured in diffusers (von Platen et al., 2022), while ours uses CFG 7.5. All models are 1024px, except SDXL-Turbo is 112px.
For image evaluation, we follow the evaluation protocol in previous diffuusion distillation works (Sauer et al., 2024; 2025) and generate samples using 300 randomly selected prompts from PartiPrompt (Yu et al., 2022a) and DrawBench (Saharia et al., 2022). We generate 3 images per prompt. For video evaluation, we generate one video per 96 custom prompts. We have raters to evaluate each comparison pair. The entire user study takes 50,328 sample comparisons.
Additionally, following the previous works (Lin et al., 2024; Sauer al., 2024; 2025), we also report the FID (Heusel et al., 2017), PFID (Lin al., 2024), and CLIP (Radford et al., 2021) metrics COCO dataset (Lin et al., 2014). Note that we find these automatic metrics to be less ac- curate than user studies for assessing the model's actual performance. We provide the results and discussion in Ap- pendix A. We also provide video quantitative metrics on VBench (Huang et al., 2024b) in Appendix B.
4.1. Qualitative Evaluation
For image generation, we first compare our APT's one step with our original diffusion model's 25 steps in Figure 2. We observe that the diffusion model with CFG often gen- erates over-exposed images, rendering the images appear synthetic. In comparison, the APT model tends to generate images with a more realistic tone. Figure 3 further compares our method with other one-step image generation methods. Our method shows advantages in preserving details and
Image Generation: One-Step v.s. 25-Step. We first com- pare all one-step model against their corresponding original diffusion model in 25 steps in Table 1. The table shows that all existing one-step methods have degradation in all
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Diffusion Adversarial Post-Training for One-Step Video Generation
Ours Diffusion
FLUX Dev 25 Steps
SD3355
SDXL Diffusion 25 Steps
APT Step
Schnell Step
Diffusion 25 Steps
Turbo Step
DMD2 Step
Hyper Step
Lightning Step
Nitro Step
25 Steps
(a) frustrated child.
(b) The city of London.
(c) A close-up of the eyes of an owl.
Figure 3: Image generation comparison across methods and models. We show results of 1-step generation and the corresponding diffusion model 25-step generation. Our method is significantly better in image details and among the best in structural integrity. More results are in ??.
Diffusion
Adversarial Post-Traine Trainer 2 Steps (2NFE)
25 Steps (5ONFE)
Step (INFE)
(a) Good case: Adversarial post-training enhances details and realism. Western princess, with sunlight shining through the leaves on her face, facial close-up.
(b) Average case: Adversarial post-training can produce the scene but with degradation in structure and text alignment. First-person perspective, the camera passes through classroom entering the school playground.
(c) Failure case: Adversarial post-training can fail at some prompts. A terracotia warrior holds white paper in one hand, and the paper flutters the wind. The background a museum.
Figure 4: Video generation results. Adversarial post-training can improve visual fidelity, i.e., details and realism, but few-step generation still has degradation structure and text alignment. More full videos are provided in ??.
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Diffusion Adversarial Post-Training for One-Step Video Generation
Table 1: One-step image generation compared to their cor- responding original diffusion models in 25 steps.
Table 3: Comparison of other base diffusion models with our base model for image generation. All models are evalu- ated using 25 steps. Since our model is designed to handle both video and image generation. its image generation per- formance is slightly inferior to FLUX and SD3.5.
Image One-Step v.s. Diffusion
Visual Fidelity
Structural Integrity
Text Align
Step v.s. 25 Steps
BLUX-Schhell
36.66
-2.44%
-2.8%
3D3.5-Large-Turbe
99.4%
-3.11%
-20.4%
Image Ours v.s. Others
Visual Fidelity
Structural Integrity
Text
SDXI-DDMD2
-9.3%
-16.8%
-4.6%
25 Steps
Align
SDXL-HHyper
-8.8%
-12.3%
-211%
SDXI-Lightting
-7.7%
-15.1%
-17.4%
FLUX
-11.4
-32.9%
11.99%
SD3.5-Large
-36.2%
-25.6%
-14.2%
SDXL-Nito-Reaalsmm
-21.6%
-22.7%
-5.6%
SDXL-Turbo
-80.1
-14.9%
-1.2%
SDXL
+12.1%
+1.2%
+0.2%
APT (Ours)
+37.2%
-13.1%
-8.1%
Table 4: Comparison of one-step (and two-step) video gen- eration with the original 25-step diffusion models.
Table 2: Comparison the state-of-the-art one-step image generation. Both absolute and relative preference scores (adjusted for base model performance) are presented. The methods are sorted by average preference.
Video
Visual Fidelity
Structural Integrity
Text
Steps
and Steps v.s. 25 Steps
Align
?
2
+32.3%
-31.3%
-9.4%
Image Ours v.s. Others
Visual Structural
Text Align
Average
APT (Ours)
1
+10.4%
-38.5%
-8.3%
Step
Fidelity Integrity
Absolute
FLUX-Schnell
+35.7%
-21.5%
-28. 1
-4.6%
SDXL-DMD2
+34.7%
+10.3%
-1.88
+11.1%
performance comparable to the state-of-the -frte-- in one-step
OOL--Nitro-Realisee
+24.6%
+16.7%
-4.9%
+12.1
image generation. On average, ranks second in absolute preference, trailing FLUX-Schnell and ranks first in relative
SDXL--Hyper
+43.6%
+4.1%
-6.7%
+13.7%
SDXL--igghtnnn
+34.1%
+14.1%
+11.4%
+19.9%
SDXL-Turbo
+68.9%
+14.9%
-7.9%
+25.3%
preference. Compared to the baseline methods, our model is preferred for its visual fidelity and structural integrity but is less preferred in text alignment. The weaker text alignment is a limitation of the APT method. Further discussion on the causes can be found Section 5.5.
SD3.5-Large-
+97.8%
+7.7%
-16.7%
+29.6%
Relative
SDXL-DDM2
+22.6%
+9.1%
-12.0%
+6.6%
HOLL-Nitro-Realiae
+12.5%
+15.5%
-5.1%
+7.6%
SDXL-Hyper
+31.5%
+2.9%
-6.9%
+9.2%
SDXL--ightning
+22.0%
+12.9%
+11.2%
+15.4%
The relative preference score is introduced to reduce the bias that a strong base model can unfairly influence the eval- uation of the one-step acceleration method. Thus we report the base model rates in Table 3. Because our base model handles both video and image generation, uses different training data, and has not undergone human- prefuurn-eeeeeeeeeee tun- ing, our diffusion model is weaker compared to FL.UX and SD3.5. We highlight these differences in the base model for interpreting the comparison of one-step image generation in Table 2.
SDXII-Turbo
+56.8%
+13.7%
-8.1%
+20.8%
PL.UX-Schnell
+77.1%
+11.4%
-9.2%
+26.4%
LD3.S-Largo
+134.0%
+33.3%
-2.5%
+54.9%
three criteria. In terms of structural integrity, our method has degradation but is less than almost all existing methods except for SDXL-Hyper. Our method is weaker in text align- ment but is still mid-tier. It is worth noting that our model is the only one to achieve a more favorable evaluation criterion (visual fidelity), aligning with our qualitative observation that APT enhances details and realism. The improvement over the original diffusion model can be attributed our method's s approach, which forgoes using the diffusion model as teacher and instead performs direct adversarial training on real data. More discussions are provided in Section 5.3.
One-step Video Generation. Table 4 compares the one- step and two-step video generation results compared to the original diffusion baseline using 25 steps. The trend is simi- lar to the image performance, where our model outperforms the original diffusion model in visual fidelity, but has degra- dation in structural integrity and text alignment. Despite the degradation, the videos generated in one step maintain decent quality at x280x720 resolution. We refer readers to view the videos on our website. The degradation in struc- tural integrity appears to be more severe in videos compared to images since it now involves motions. Our work is a preliminary proof concept. We emphasize the need for further research to advance one-step video generation.
One-Step Image Generation: Comparison to the State- of-the-Art. In Table 2, we compare our one-step gen- eration to the state-of-the-art one-step image generation models. We show both the absolute preference score and the adjusted relative change based on their corresponding diffusion model baseline, which will be described later.
The results in Table 2 demonstrate that our method achieves
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Diffusion Adversarial Post-Training for One-Step Video Generation
5. Ablation Study and Discussion
5.3. The Reason for Visual Improvement
5.1. The Effect of Approximated RI Regularization
We find our APT model mainly resolves the over-exposure and over-saturating tone issue resulting from CFG. We find APT can help the generator learn distributions closer the real data. More discussions are in Appendix H.
We find that the approximated RI regularization is critical for maintaining stable training. Without this regularization, training collapses rapidly. As shown in Figure 5, the black curve represents the discriminator loss without regulariza- tion, which quickly approaches zero compared to the green curve that includes the regularization. When the discrimi- nator loss approaches zero, the generator produces colored plates, as depicted on the right side of F. 5.
5.4. The Cause of Structural Degradation
We take the one-step image model and interpolate the input noise to generate latent traversal visualizations. We find structural incorrectness often occurs during the smooth tran- sition between modes, where the diffusion model switches between modes more sharply. We hypothesize the generator being under-capacitated one of the main causes of the degradation in structural integrity. More discussions are in Appendix I.
Mode A
Transitioning
Mode B
Figure 5: Without approximated RI regularization, the dis- criminator loss reaches zero (black) and the training col- lapses. With approximated RI regularization, the discrimi- nator loss does not reach zero (green).
5.2. Discriminator Design
We first experiment with using different depths of the pre- trained diffusion model as our discriminator. Figure 6 shows that a deeper discriminator leads to better image quality.
Figure 8: One-step generator has limited capacity to make sharp switches between modes.
5.5. Analysis of Text Alignment Degradation
Helf-DDeth Discriminator
Pero-Third-Depeee Discriminator Layer 18. 22, 26th
Full-Depth Discriminator Layer 16, 26, 36th
Previous research has found CFG can force the generated distribution to have even stronger text alignment than the real data distribution, ie., samples looking canonical (Kar- ras al., 2024; Lin & Yang, 2023). APT learns from the real distribution without CFG's boosting effect. Our dataset has been re-captioned, text alignment, though not as strong as CFG. Furthermore, the problem in Section 5.4 also has negative impact on the alignment of the text. We have conducted additional experi- ments and have provided more discussions in Appendix
Layer 14, 16, 18th
APT model still has acceptable
Figure A deeper discriminator that includes the full depth of the pre-trained network leads better quality.
6. Conclusion and Limitations
We then verify the effectiveness of using multilayer features. Figure shows that using multilayer features can improve structural correctness.
We propose Adversarial Post-Training (APT) for single-step generation of both image and video. Our method incorpo- rates several architectural enhancements and an approximate R1 regularization, which are crucial for training stability.
Last-Layer Discriminator Layer 36th
Multi-Layer Discriminator Layer 16, 26, 36th
To the best of our knowledge, we present the first proof of concept for generating high-resolution videos (1280x720 24fps) in single step. However, we identify several limi- tations in the current approach. First, due to computational constraints, we were only able to train the model to gen- erate videos for up to two seconds. Second, the one-step generation still has degradation in structural integrity and text alignment, which we aim to address in future works.
Figure 7: The multi-layer discriminator improves structural integrity.
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Diffusion Adversarial Post-Training for One-Step Vidco Generation
Acknowledgment
transformer for any aspect ratio and resolution. Advances in Neural Information Processing Systems, 36, 2024.
We thank Jiashi Li, Zuquan Song, and Junru Zheng for managing the compute infrastructure. We thank Yanzuo Lu, Meng Guo, Weiying Sun, Shanshan Liu and Yameng Li for image evaluation. We thank Ruiqi Xia, Donglei Ji, Juntian Chen, and Songyan Yao for video evaluation.
Esser, P. Kulal, S., Blattmann, A., Entezari, R., Muller, J., Saini, H., Levi, Y.. Lorenz, D., Sauer, A., Boesel, F, Podell, D., Dockhorn, T., English, Z. Lacey, K., Goodwin, A., Marek, Y., and Rom- bach, R. Scaling rectified flow transformers for high- resolution image synthesis. ArXiv, abs/2403.03206. 2024. URL https: semanticscholar.
Impact Statement
This paper presents work whose goal to advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
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A. Image Quantitative Metrics
Table 5: Quantitative metrics on COCOSK across different
models and methods for image generation. We find the metrics inaccurately capture the actual performance of the model. Discussion are provided in Appendix A.
Following the previous works (Sauer et al., 2025; Chen al., 2024; Yin et al., 2024a; Lin al., 2024), we calcu- late Fréchet Inception Distance (FID) (Heusel al., 2017), Patch Fréchet Inception Distance (PFID) (Lin et al., 2024), and CLIP score (Radford et al., 2021) on COCO dataset (Lin et al., 2014). We notice some of the works use COCO- 5K (Sauer et al., 2025; Chen et al., 2024) while others use COCO-10K (Lin et al., 2024; Yin et al., 2024a). We provide both in Tables and Specifically, we take the first 5,000 and 10,000 captions from the COCO 2014 validation dataset as the generation prompts. FID is calculated between the generated images and the ground-truth COCO images. The ground-truth images are cropped to square aspect ratio before resizing to 299px for the inception network (Szegedy et al., 2015). Resizing is done properly with established library (Parmar et al., 2021). Patch FID (PFID) metries (Lin et al., 2024; Yin al., 2024a) center-crops 299px patches without resizing measure image details. For 512px mod- els, the results are bilinear upsampled to 1024px before center-crop following prior work (Lin et al., 2024). For the CLIP score, we follow the previous works (Sauer et al., 2025) to use the Mtion/CLIP-VVT-gee 1---ai4222---1122222 12B-b42K model. We use each model's default CFG (Ho & Salimans, 2021) as configured in diffusers (von Platen et al., 2022), e.g., FLUX uses 3.5 baked-in CFG. Our diffusion model uses 7.5 CFG. We also verify that our metric calculations match what was reported in the original publications, e.g,, DMD2's reported FID 19.01 is close to our report of 18.10. SDXL--Lghttinn'' reported FID 22.61 is close to our re- port of 23.40, etc. The difference can be attributed to the randomness of the seed and the selection of prompts.
Method
Steps
FID
PFID
CLIP
FLUX-Dev
25
31.8
38.7
32.9
FLUX-Schnell
24.9
30.0
33.7
SD3.5-Large
25
25.1
30.8
33.8
SD3.5-Turbo
61.6
174.5
30.4
SDXL
25
25.1
28.7
33.9
SDXL-DMMD2
24.1
33.0
34.1
SDXL-Hyper
36.9
41.8
33.1
SDXL-Lightning
28,9
36.9
31.9
SDXL-Nitro-Rede Realism
26.4
32.8
33.9
SDXI.-Turbo (512px)
28.4
66.0
33.8
Our Diffusion
25
26.9
30.6
33.2
Our Consistency
119.6
164.6
22.3
Our APT
27.9
34.6
32.3
Table 6: Quantitative metrics on COCO1OK for image gen- eration. The metries also inaccurately capture the actual performance of the model. Discussion provided in Ap- pendix A.
Method
Steps
FIDJ
PFID.
CLIP
FLUX-Dev
25
26.3
32.8
32.9
FLUX-SSchnel
1
18.8
23.7
33.7
SD3.5-Large
25
19.2
23.7
33.8
SD3.5-Turbo
55.7
170.9
30.4
SDXL
25
19.2
22.5
33.9
SDXL-DMD2
18.1
26.2
34.0
SDXL-Hyper
31.4
35.3
33.2
SDXL--ightning
23.4
30.4
31,9
We find that the metries can be far off from the model's actual performance. For example, FLUX 1-step surpasses FLUUX 25-step in all metrics. However, this strongly contra- dicts our human perception and evaluation in Table 1, and our observation that FLUX 1-step has visible degradation in visual quality and structural integrity. Furthermore, the met- rics suggest that SDXL. 25-step is better than FLUUX 25-step in all metrics. This also contradicts our human evaluation in Table 3 and our observation that SDXL should have weaker performance than FLUX.
EDXL-Nitro-Realle
20.2
26.2
33,9
SDXL--Turbo (512px)
22.8
35.7
33.8
Our Diffusion
25
20.7
24.7
33.1
Our Consistency
114.1
161.3
22.3
Our APT
22.1
28.5
32.3
carefully, and we leave the exploration for better metrics to future works.
B. Video Quantitative Metrics
We notice that most previous diffusion distillation publica- tions (Sauer et al., 2024; 2025; Lin et al., 2024; Ren et al., 2024; Chen et al., 2024) are conducted on the SDXL model. The metrics within the SDXL family of models appear more reasonable, potentially concealing the issue. Yet, this does not mean that the metrics are valid within the same archi- tecture family of models, as the metrics wrongly identify FLUX 1-step as being better than FLUX 25-step.
For video generation, we also provide VBench (Huang et al., 2024b) quantitative metries in VBench in Table 7. Specifically, we generate 5 videos for each of VBench's 946 prompts and report the VBench scores. As the table shows, APT significantly outperforms the baseline consis- tency distillation I INFE, 2NFF, and 4NFE. We observe that the consistency INFE baseline generates lower-quality re- sults (e.g.. blurry videos), and these issues persist even when increasing to 4NFE. These metrics verify the effectiveness of APT post-training compared to the baseline. Even when
Therefore, we primarily rely on human evaluations for our work. We caution researchers to interpret these metrics
14

---

Diffusion Adversarial Post-Training for One-Step Video Generation
compared base model SONFE (25 steps CFG), despite this being unfair comparison, APT INFE achieves a comparable total score (82.00 vs. 82.15).
50
150
350
450
550
650
Table 7: Quantitative metrics on VBench for video gener- ation. Comparison between our APT model and the diffu- sion/consistency baselines.
Figure Training progression measured by generator up- dates. (EMA top, Mon-EMA bottom)
Total Score
Quality Score
Semantic Score
Method
Steps
E. The Effect of the Batch Size
Diffusion
25
82.15
84.36
73.31
For images, our early experiments suggest that larger batch size improves stability and structural integrity, confirming with previous research (Xu et al., 2023b; Kang et al., 2023). For videos, we find that using small batch size of 256 leads to mode collapse as shown in Figure 10 whereas a large batch size of 1024 does not. Therefore, our final training adopts a large batch size of 9062 for images and a batch size of 2048 for videos.
77.97
81.93
62.10
Consistency
74.20
78.83
55.69
67.05
73.78
40.15
81.85
84.39
71.70
APT
82.00
84.21
73.15
C. Inference Speed
Video Batch Size 256
Video Batch Size 1024 Seed A
Seed A
Seed B
Seed B
Table 8 shows the inference speed of our model on different numbers of H100 GPUs with parallelization. Our model generate x280 720 24fps 2-second video in 6.03s on single H100 GPU. With 8 H100 GPUs, it can achieve real-time. More optimization can be applied to make it more efficient.
Figure 10: A large batch size prevents mode collapse. A small batch size has mode-collapsed across prompts and seeds.
Table 8: Inference speed under different numbers of H100 GPUs for one-step two-second 1280x720 24fps video gen- eration.
F. The Effect of the Learning Rate
# of H100 Component
Seconds
Text Encoder
0.28
We first search for learning rates in image settings. We find that 1e-6 and 1e-5 are either too small or too large and settle on 5e-6 for stable training. Then we experiment with freezing lowering the learning rate for the discrim-
DiT
2.65
1
VAE
3.10
Total
6.03
Text Encoder (no parallelization)
0.28
inator backbone but find that it is best to train the entire discriminator network, as shown in Figure 11.
DiT
0.73
VAE
1.19
Total
2.20
Freeze Backbone
Low Backbone LR le-6, 5e-6
Full LR
Text Encoder (no parallelization)
0.28 0, Se-6 (new param)
5e-6, 5e-6
DiT
0.50
8
VAE (only 4-GPU parallclization)
1.19
Total
1.97
D. The Effect of Training Iterations and EMA
Figure 9 shows the model adapts fast. For the non-EMA model, even after 50 updates, it is able generate sharp images. The EMA model generally performs better than the non-EMA. We find the quality peaks at 350 updates for the EMA model, and training it longer leads to more structural degradation.
Figure 11: It better to train the entire discriminator than to freeze or lower the learning rate of the backbone. Freezing the backbone produces undesirable artifacts (zoom in for detail), Lowering the backbone learning rate causes slow convergence.
15

---

Diffusion Adversarial Post-Training for One-Step Video Generation
G. Understanding the Model Internals
transition between modes. This is likely because the one- step model effectively is much shallower in depth, has less nonlinearity, and has a lower capacity for making drastic changes. This effect has also been observed by a prior work (Lin et al., 2024). We hypothesize the generator being under-capacitated is one of the main causes of the degrada- tion in structural integrity. As Figure 8 shows, we find that structural incorrectness often occurs during the transition between modes. This negatively affects the text alignment, as hallucinations may occur, e.g., one object might appear as two during the transition phase. The training loss sup- ports this hypothesis, where the discriminator can always differentiate before convergence. We aim to address this issue in future works.
We freeze the model and add an additional linear projection on every layer. is trained to match the final layer's latent prediction with mean squared error loss. This helps us visualize the internals of our model. As Figure 12 shows, the network's shallow layers generate the coarse structure and the deeper layers generate the high-frequency details. This is similar to the iterative generation process of diffusion models, except in our model the entire generation process compressed within the transformer layers in a single forward pass.
Layer 6
Layer 12| Layer 18 Layer 24
Layer 30
Layer 36
J. Additional Explorations on Text Alignment
We have explored several techniques to improve text align- ment but found them to be ineffective. These include: (1) providing the discriminator with unmatched conditional pairs to penalize misalignment, as proposed by (Kang et al., 2023; Sauer et al., 2023). However, we choose not to adopt this approach because our early experiments showed min- imal improvements; (2) incorporating CLIP loss (Radford et al., 2021). Our experiments indicate that it could nega- tively impact visual fidelity, leading to poorer details and artifacts. We leave further investigation to future research.
Figure 12: The intermediate result of each transformer layer inside the one-step image generation model.
H. More Discussion on Visual Improvement
Diffusion models without classifier-free guidance (CFG) (Ho & Salimans, 2021) generate very poor samples (Lin & Yang, 2023). CFG is used ubiquitously to boost perceptual quality and text aligmment, but recent works have shown that it can push the generated distribution away from the training distribution, resulting in samples that appear syn- thetic, saturaatr,,,,, and canonical (Lin al., 2023; Karras al., 2024). A recent work (Lin & Yang, 2023) has elu- cidated that the cause of diffusion models generating poor samples without CFG may be rooted in the mean squared error (MSE) loss objective. It has also demonstrated the use of perceptual loss can better learn the real data distribution. We hypothesize that adversarial training can be viewed as an extension of this work, where does not have the MSE issue and the discriminator a learnable, in-the-loop, per- ceptual critic. This helps the generator to learn distributions closer to the real training data.
I. More Discussion on Structural Degradation
We take the one-step image model and interpolate the input noise z to generate latent traversal videos. The videos are available on the webpage described in ??. Unlike GAN models which normally have a low-dimensional noise 2, our model has very high-dimensional E IR '''' x xe'.
We find interpolation on the high-dimensional still pro- duces traversal videos with semantic morphing. Compared to the diffusion model which switches between modes very quickly, our one-step generation model has much smoother
16


Reviews:
[{"review_id": "FaanDOB9c9", "reviewer": "Reviewer_RHEj", "summary": "The paper presents a post-training approach to transform a pretrained video diffusion model (based on DiT architecture) into a one-step generation model, unlike traditional diffusion models requiring multiple (or at least a few) steps. Unlike many existing distillation methods that train a separate student model under the supervision of a teacher model, this work directly applies post-training to a pretrained model using adversarial training (GAN-based framework). Notably, the authors introduce techniques to stabilize training, particularly an approximated version of R1 regularization, as the standard R1 loss is unsuitable for large-scale training scenarios.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comparison with Consistency Baseline**\n\nWe respectfully point out that consistency distillation (CD, including methods such as LCM) has been extensively studied in prior works (e.g., DMD, DMD2, Lightning, Hyper-SD, LADD), which consistently show that CD struggles to produce sharp results in a single step. For instance, see Figure 3 in [Lightning](https://arxiv.org/pdf/2402.13929). This has also been shown in Figure 9 of our submission which illustrates that as training progresses, the initial CD model generates noticeably blurry outputs in one step. Therefore, in our work, we choose to compare directly against state-of-the-art methods rather than the CD baseline.\n\nHowever, following the reviewer's suggestion, we have included the CD-initialized model as a baseline. We compute metrics: FID, PFID, and CLIP scores, on both the COCO-10K and COCO-5K benchmarks. As shown in the table below, the results are consistent with those previously reported in the paper. We will incorporate them in the revised paper.\n\nIn addition, we have included a video comparison between the CD and APT models on the VBench benchmark. Please refer to our response to `Reviewer afjC` for the detailed results.\n\n| *COCO-10K*         | FID↓   | PFID↓  | CLIP↑ |\n|-------------------|-------:|-------:|------:|\n| Diffusion 25step  | 20.7   | 24.7   | 33.1  |\n| Consistency 1step | 114.1  | 161.3  | 22.3  |\n| APT 1step         | 22.1   | 28.5   | 32.2  |\n\n| *COCO-5K*          | FID↓   | PFID↓  | CLIP↑ |\n|-------------------|-------:|-------:|------:|\n| Diffusion 25step  | 26.9   | 30.6   | 33.2  |\n| Consistency 1step | 119.6  | 164.6  | 22.3  |\n| APT 1step         | 27.9   | 34.6   | 32.3  |\n\nMore visualization comparison between CD and APT is provided in this [external link](https://rebuttal.blob.core.windows.net/assets/comparison.html).\n\n**Differences to Existing Methods**\n\nWe would like to elaborate on the differences between our methods and existing methods (e.g. DMD CVPR24):\n\n1. **Simplicity**: DMD combines variational score distillation (VSD) and rectified flow (RF) objectives, as VSD alone can lead to mode collapse while RF can cause sharpness issues. DMD2 changes to use both VSD and adversarial objectives, but this setup requires training 3 networks: the student, the VSD negative score model, and the adversarial discriminator. In contrast, our method adopts a simpler approach, using only an adversarial objective with CD initialization. This streamlined design allows the model to adapt quickly—in just 350 iterations (see Figure 9 in the paper).\n\n2. **Post-training vs. distillation quality**: Existing methods (such as DMD, DMD2, Lightning, Hyper, ADD, and LADD) distill the teacher results, where the teacher model is the quality upper-bound. Our method is adversarial post-training (APT) against real data, and we demonstrate that it is able to surpass the teacher diffusion model in some criteria. The discussion is provided in Appendix H.\n\n3. **First large-scale video generation with 1NFE**: Existing methods like DMD and LADD require precomputing teacher noise-sample pairs, which can be computationally expensive—especially for high-resolution video tasks. In contrast, our method trains directly on real data, avoiding these overheads. This enables us to be the first to demonstrate one-step generation using a large-scale T2V model, achieving 1280×720 resolution at 24 fps for 2-second videos.\n\n4. **Closer to traditional GANs**: Existing adversarial distillation methods generally follow the DiffusionGAN approach, which may appear similar to GANs but differs in key ways: 1) noise corruption is added to the discriminator inputs (e.g., LADD, UFO-GAN, Lightning), and 2) it freezes the discriminator backbone and only trains the generator (e.g., ADD, LADD). In contrast, our method more closely resembles classical GANs: the discriminator is fully trainable, and no noise corruption is applied to its inputs. Additionally, we introduce an approximate R1 loss, inspired by traditional R1 regularization, which significantly improves training stability. We hypothesize that this design contributes to the reduction of visual artifacts in our one-step generation, as compared to existing works (LADD, Lightning, etc.), as illustrated in Figure 3.\n\nWe hope the above points clarify the differences from related work and substantiate the contributions of our method.", "claims_and_evidence": "The main claims include:\n\n- Effective conversion from multi-step diffusion to one-step generation through adversarial post-training.\n- Improved training stability due to the introduced approximated R1 loss.\n\nThe authors provide qualitative evidence supporting the overall effectiveness of their final model. However, there is a gap: the paper lacks direct comparison between the adversarially post-trained model and its initial consistency-distilled baseline. This omission makes it difficult to clearly attribute performance gains to the proposed adversarial post-training strategy alone.", "methods_and_evaluation_criteria": "The proposed adversarial post-training method and the approximated R1 regularization are well-chosen and relevant to the stated problem. Evaluation criteria (qualitative visual comparisons with state-of-the-art methods) align with standard practice. However, the evaluations suffer from a crucial shortcoming: absence of direct comparative evaluation against the initial consistency-distilled model, undermining the strength of claims regarding performance improvements.", "theoretical_claims": "No explicit theoretical claims or proofs are provided.", "experimental_designs_or_analyses": "While the experimental design convincingly shows the practical engineering value and demonstrates stability gains from the approximated R1 loss, it lacks thorough comparative analyses. Specifically, the paper fails to provide direct visual and quantitative comparisons between the final adversarially post-trained model and the initial consistency-distilled baseline.", "relation_to_broader_scientific_literature": "The paper relates closely to recent literature on accelerated diffusion models and distillation techniques. However, the distinction made by authors between their post-training approach and existing teacher-student approaches (e.g., DMD, CVPR 2024) is somewhat ambiguous since many student models are similarly initialized from teacher weights.", "essential_references_not_discussed": "No essential missing references identified.", "other_strengths_and_weaknesses": "**Strengths:**\n\n- Practical approach achieving effective one-step video generation.\n- Useful technical innovation in approximated R1 regularization, addressing large-scale training stability issues.\n\n**Weaknesses:**\n\n- Inadequate comparative analysis against the baseline consistency-distilled model.\n- Limited novelty regarding model initialization approach relative to existing methods (e.g., DMD).", "other_comments_or_suggestions": "To significantly strengthen the paper, the authors should provide explicit qualitative and quantitative comparisons between the final adversarially post-trained model and the initial consistency-distilled baseline, clarifying whether performance genuinely improves.", "questions_for_authors": "1. Could you provide direct qualitative and quantitative comparisons between your final adversarially post-trained model and the initial consistency-distilled baseline? This is crucial for evaluating the genuine effectiveness of your proposed method.\n\n2. Given that the adversarial post-training starts from an already strong consistency-distilled model, can you explicitly demonstrate if and how the proposed method improves video quality rather than merely preserving it?\n\n3. Could the authors elaborate further on the fundamental difference between their post-training approach and existing distillation methods like DMD, which also initialize student models with teacher weights?", "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": "DKVkBuk4MV", "reviewer": "Reviewer_Df84", "summary": "The paper introduces Adversarial Post-Training (APT), a method that accelerates diffusion-based video generation from multiple inference steps to a single step while preserving high-quality visual output. The approach builds on a pre-trained diffusion model and uses direct adversarial training with real data.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Related Works**\n\nWe appreciate the reviewer pointing out the related works. We will add all of them in the revised paper.\n\n* SF-V and OSV are 1-step image-to-video generation. We initially did not include them because our work focuses on text-to-video generation. Both of these works are based on UNet based Stable Video Diffusion and can generate up to 1024x576 for 14 total frames. In comparison, our works can generate 1280x720 of total 48 frames (2s 24fps) from text prompts.\n\n* PCM is an adversarial consistency distillation approach very similar to Hyper and our paper has conducted extensive comparison with Hyper. We notice that the paper has also experimented with distillation on AnimateDiff along with AnimateLCM and AnimateDiff-Lightning. We will add this paper to our related works.\n\n* InstantPortrait focuses on 1-step image editing tasks with contributions primarily addressing task-specific problems, which may not be directly relevant to our problem.\n\n\n**Comparison Against Other Distillation Methods**\n\nThe reviewer suggested a comparison against ADD, LADD, and UFOGen. In fact, both ADD and LADD are already included in our submitted paper. Specifically, the ADD method is referred to as SDXL-Turbo and the LADD method as SD3-Turbo, following the naming conventions used by Stability AI [[1](https://stability.ai/news/stability-ai-sdxl-turbo),[2](https://arxiv.org/abs/2403.12015)]. We apologize for any confusion and will clarify this in the revised version. We did not include UFOGen in our comparisons, as it was shown to performce worse than DMD2 in the DMD2 paper. Since our paper includes DMD2 as a baseline, we believe this provides a sufficient point of comparison.\n\n\n\n**VBench Results**\n\nWe have provided VBench metrics for the rebuttal. Please refer to the rebuttal response to `Reviewer afjC`. These results will be included in the revised version of the paper.\n\n**Other Clarifications on Our Claims**\n\nAbout Ln42-Ln45: We have acknowledged in Ln70-Ln76 that UFOGen is closest to our work which only applies adversarial training on real data. We also elaborated our differences compared to UFOGen. Specifically, our discriminator design is closer to the traditional GAN, and our APT model can surpass the teacher model in some criteria which validates the proposed adversarial post-training.\n\nAbout Ln56-Ln57: We have elaborated in Appendix B how the traditional COCO FID and PFID metrics in Table 5 and 6 do not fully capture the model performance and thus opt for human evaluation. We leave the exploration for better automated metrics to future works. Our claim is based on Table 1 and 4, where our APT model shows improved visual fidelity compared to the original diffusion model.", "claims_and_evidence": "The claim from Ln 42 to Ln 45, “It is important to notice the contrast to existing diffusion distillation methods, which use a pre-trained diffusion model as a distillation teacher to generate the target.” is not accurate. For example, UFOGen, SF-V do not require the pre-trained diffusion model to generate the target.\n\nThe claim from Ln 56 to Ln 57, “APT demonstrates the ability to surpass the teacher by a large margin in some evaluation criteria” seems conflicted with the experiments in Appendix Table 5.", "methods_and_evaluation_criteria": "While the paper claims superior video generation quality, most quantitative comparisons are conducted against image models in the image domain. A user study and evaluations on benchmarks like VBench comparing the method with other open-source text-to-video generation models (e.g. opensora, hunyuan video, and etc.) would better demonstrate its effectiveness.", "theoretical_claims": "Looks good to me.", "experimental_designs_or_analyses": "Comparisons with other distillation methods would be helpful, (e.g. ADD, LADD, UFOGen).", "relation_to_broader_scientific_literature": "Adversarial post-training to reduce the number of inference steps of diffusion models has been widely studied.", "essential_references_not_discussed": "Lai, Zhixin, Keqiang Sun, Fu-Yun Wang, Dhritiman Sagar, and Erli Ding. \"InstantPortrait: One-Step Portrait Editing via Diffusion Multi-Objective Distillation.\" In *The Thirteenth International Conference on Learning Representations*.\n\nZhang, Zhixing, Yanyu Li, Yushu Wu, Anil Kag, Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin et al. \"Sf-v: Single forward video generation model.\" *Advances in Neural Information Processing Systems* 37 (2024): 103599-103618.\n\nMao, Xiaofeng, Zhengkai Jiang, Fu-Yun Wang, Wenbing Zhu, Jiangning Zhang, Hao Chen, Mingmin Chi, and Yabiao Wang. \"Osv: One step is enough for high-quality image to video generation.\" *arXiv preprint arXiv:2409.11367* (2024).\n\nWang, Fu-Yun, Zhaoyang Huang, Alexander Bergman, Dazhong Shen, Peng Gao, Michael Lingelbach, Keqiang Sun et al. \"Phased Consistency Models.\" *Advances in Neural Information Processing Systems* 37 (2024): 83951-84009.", "other_strengths_and_weaknesses": "None.", "other_comments_or_suggestions": "None.", "questions_for_authors": "None.", "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": "OVWTCKNeiw", "reviewer": "Reviewer_afjC", "summary": "This paper proposes an adversarial finetuning framework for one-step T2I and T2V generation for the flow denosing model, which introduces the insights of designing the discriminator and the model and how to stabilize the model training. And it achieves an exciting performance compared with other few-step models.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Video Comparisons**\n\nWe would like to clarify that existing research on diffusion acceleration has primarily focused on the image domain. To the best of our knowledge, no prior studies have proposed high-resolution, one-step video generation methods, and consequently, no suitable baselines exist for comparison. Therefore, in our submission, we evaluate our method using the image generation task, for which comparable prior works are available.\n\n\nWe acknowledge the reviewer’s perspective. Following the reviewer’s suggestion, we have included a comparison with the consistency distillation baseline and our base model.\nThe evaluation follows the public [VBench](https://vchitect.github.io/VBench-project/) protocol. Specifically, we generate 5 videos for each of VBench's 946 prompts and report the VBench scores in the table below.\n\n\nAs the table shows, APT significantly outperforms the baseline consistency distillation 1NFE, 2NFE, and 4NFE. We observe that the consistency 1NFE baseline generates lower-quality results (e.g., blurry videos), and these issues persist even when increasing to 4NFE. This result verify the effectiveness of APT post-training compared to the baseline.\n\nEven when compared to our base model at 50NFE (25 steps + CFG)—despite this being an unfair comparison—our APT 1NFE achieves a comparable total score (82.00 vs. 82.15) and even performs better on some metrics.\n\n\nWe will add the table and the analysis in the revised paper.\n\n| *VBench* | Total Score | Quality Score | Semantic Score | Multiple Objects | Overall Consistency | Spatial Relationship | Temporal Style | Object Class | Dynamic Degree | Aesthetic Quality | Human Action | Scene | Imaging Quality | Background Consistency | Subject Consistency | Temporal Flickering | Motion Smoothness | Color | Appearance Style |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Ours APT 1NFE | **82.00** | 84.21 | 73.15 | 61.89 | 26.11 | 71.78 | 23.20 | 92.41 | 62.78 | 63.04 | 90.20 | 41.19 | 69.05 | 96.38 | 97.26 | 98.57 | 98.54 | 88.98 | 19.31 |\n| Ours APT 2NFE | 81.85 | 84.39 | 71.70 | 59.60 | 25.70 | 71.95 | 23.88 | 91.30 | 68.61 | 62.68 | 87.00 | 40.10 | 69.60 | 95.89 | 96.54 | 98.48 | 98.49 | 85.92 | 18.84 |\n| Consistency 1NFE | 67.05 | 73.78 | 40.15 | 4.85 | 17.47 | 19.38 | 15.33 | 39.13 | 20.56 | 41.96 | 33.20 | 10.78 | 42.95 | 96.80 | 97.39 | 98.72 | 98.35 | 86.93 | 21.33 |\n| Consistency 2NFE | 74.20 | 78.83 | 55.69 | 23.78 | 22.88 | 44.21 | 20.96 | 63.34 | 33.33 | 54.12 | 63.60 | 24.48 | 59.93 | 96.21 | 96.33 | 98.55 | 98.29 | 88.88 | 19.20 |\n| Consistency 4NFE | 77.97 | 81.93 | 62.10 | 37.30 | 23.73 | 57.98 | 22.16 | 77.58 | 53.61 | 59.35 | 74.00 | 25.81 | 66.38 | 96.37 | 95.97 | 98.20 | 98.15 | 90.43 | 18.35 |\n|-|\n| Diffusion Base 50NFE | 82.15 | 84.36 | 73.31 | 71.68 | 26.09 | 74.95 | 23.87 | 90.49 | 75.56 | 62.94 | 89.60 | 33.63 | 69.85 | 97.02 | 96.44 | 97.61 | 97.54 | 88.69 | 18.93 |", "claims_and_evidence": "yes", "methods_and_evaluation_criteria": "yes", "theoretical_claims": "Yes, they look good to me.", "experimental_designs_or_analyses": "The experiments seem to be not efficient; the main focus of this paper is on the one-step video generation. However, there are not many experiments on the analyses of the video part, but it focuses on the image models.", "relation_to_broader_scientific_literature": "The key ideas of this paper are most related to the UFOGEN (https://arxiv.org/abs/2311.09257), which presents the adversarial fine-tuning for diffusion models on 1-step denosing generation.", "essential_references_not_discussed": "The references are sufficient.", "other_strengths_and_weaknesses": "The strengths of this paper can be concluded as 1. concrete design of adversarial diffusion finetuning for most recent diffusion models with DiT architecture and the flow matching objective. 2. The simplified alternative R1 for stabilizing the model training.\n\nThe weakness of this paper is that there is not much experimental comparison for the video generation. 720p one step generation is exciting, but 5s of 1 step video generation would be so great.", "other_comments_or_suggestions": "Please add more experimental comparisons with other video models, at least the base model and the one-step mode on the public benchmark.", "questions_for_authors": "Please see above comments.", "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:

Meta-review: The paper received 2 Weak Accept and 1 Weak Reject score pre-rebuttal. The scores are not changed after the rebuttal. Although Reviewer afjC did not change his Weak Reject score, his response to the rebuttal was positive, and the rebuttal seemed to address his concern well.

The ACs checked and found the paper's results strong and valuable to the community. Hence, we agreed to accept the paper to ICML. The authors should include the discussions and results in the rebuttal, particularly the additional VBench results, in the camera-ready version.
Decision: Accept (poster)