TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows
Authors: Zhenglin Cheng
Peng Sun
Jianguo Li
Tao Lin
Venue: ICLR 2026 Poster (2026)
Keywords: few-step generation
text-to-image generation
multi-modal generative models
Primary area: generative models
TL;DR: We propose a simple yet effective framework for training one-step generative models without the demand of pretrained models for distillation or standard adversarial training, which is helpful when training large few-step generative models.

Abstract: Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation.
These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)).
While various few-step methods aim to accelerate the inference, existing solutions have clear limitations.
Prominent distillation-based methods, such as progressive and consistency distillation, either require an iterative distillation procedure or show significant degradation at very few steps (< 4-NFE).
Meanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models.
To this end, we propose TwinFlow, a simple yet effective framework for training 1-step generative models that bypasses the need for distillation from pre-trained models and avoids standard adversarial training, making  it ideal for building large-scale, efficient models.
On text-to-image tasks, our method achieves a GenEval score of 0.83 in 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework).
**Notably, we demonstrate the scalability of TwinFlow by transforming Qwen-Image-20B---the current largest open-source multi-modal generative model---into an efficient few-step generator**. With just 1-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and DPG-Bench benchmarks, reducing computational cost by $100\times$ with minor quality degradation.
Our code and models will be made publicly available.

Published as a conference paper at ICLR 2026
TWINFLOW: REALIZING ONE-STEP GENERATION ON LARGE MODELS WITH SELF-ADVERSARIAL FLOWS
Zhenglin Cheng .2,,4,
Peng Sun 13.4,
Jianguo Li
Tao 13.3
Westlake University Shanghai Innovation Institute
Inclusion AI
*Zhejiang University
ABSTRACT
Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video gen- eration. These models are typically built upon multi-step frameworks like diffusion and flow matching, which inherently limits their inference efficiency (requiring 40-100 Number of Function Evaluations (NFEs)). While various few-step methods aim accelerate the inference, existing solutions have clear limitations. Prominent distillation- methods, such as progressive and consistency distillation, either require an iterative distillation procedure show sigmificant degradation at very few steps (< 4-NFE). Mcanwhile, integrating adversarial training into distillation (e.g., DMD/DMD2 and SANA-Sprint) to enhance performance introduces training instability, added complexity, and high GPU memory overhead due to the auxiliary trained models. To this end, we propose TWINFLOW, a simple yet effective framework for training 1-step generative models that bypasses the need of fixed pretrained teacher models and avoids standard adversarial networks during training, making it ideal for building large-scale, efficient models. On text-to- text-to-image tasks, our method achieves a GenEval score of 0.83 1-NFE, outperforming strong baselines like SANA-Sprint (a GAN loss-based framework) and RCGM (a consistency-based framework). Notably, we demonstrate the scalability of TWINFLOW by full- parammeereer training on Qwen-Image-201 and transform it into an efficient few-step generator. With just I-NFE, our approach matches the performance of the original 100-NFE model on both the GenEval and JPG-Bench benchmarks, reducing computational cost by 100x with minor quality degradation. Project page is available here.
Code:
Models:
INTRODUCTION
Modern generative paradigms-including diffusion (Ho et al., 2020; Song et al., 2020a), flow matching (Lipman et al., 2022; Ma al., 2024), and consistency models (Song al., 2023; Lu & Song, 2024)-hall achieved state-of-the-art performance, forming the backbone of leading image and video generation systems (Peebles & Xie, 2023; Ho al., 2022; Chen al., 2024c: Xie et al., 2024a). Despite their success, these methods share critical drawback: both training and sampling demand substantial computational resources. This challenge magnified in the era of large-scale models (ModelTC, 2025). For these systems, efficient sampling is paramount, as the continuous, long- term cost of inference often surpasses the one-time training cost, directly impacting their economic viability and practical deployment (ModeITC, 2025; Xie et al., 2025a).
Numerous research efforts aim accelerate generative inference by reducing the number of sampling steps. Early single-step methods, like Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), often suffered from unstable training dynamics. To accelcrate the multi-step diffusion models.
* "Equal contributions. Work done during internship Inclusion AI, Ant Group
I Project leader.
1 Corresponning author.

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Published as a conference paper at ICLR 2026
TwinFlow 1 Step Generation Made Easy
SLOW ( 50
QWEN- IMAGE-20B
Figure 1: Results Sween-mmgg-20B-TTIINEE (NFE=2). See prompts in App. E.3.
various distillation techniques have been introduced. These range from student-teacher methods, where a compact model learns to emulate a larger one in fewer steps (Salimans & Ho, 2022; Meng al., 2022), to distribution matching distillation (e.g., DMD variants (Yin al., 2024b;a)), which uses adversarial training to directly align the model's output distribution with the real data. In parallel, powerful new paradigm consistency models (Song et al., 2023) and their variants, such as LCM (Luo et al., 2023) and PCM (Wang et al., 2024a), has emerged, explicitly designed for high-quality generation very few steps.
Despite their progress, existing few-step methods face a difficult trade-off between simplicity, effi- ciency, and quality. (a) Complexity and instability: As summarized Tab. 1, adversarial methods such GANs and DMD require auxiliary networks (e.g., discriminators) frozen teacher models. This not only introduces training instability and sensitivity to hyperparameters but also increases architectural complexity and memory overhead (c.f. Fig. 2b), hindering their scalability to large mod- els. (b) Performance degradation: Conversely, methods that train from scratch without adversarial guidance (Luo et al., 2023; Yin al., 2024a), such as consistency models, often exhibit sharp decline in quality at very low NFEs (< 4) (Chen et al., 2025c). In summary, we posit that existing methods either suffer from training instability, or require additional/frozen models (see our Tab. 1), which limits their simplicity and scalability in training large models.
To address these challenges, we propose TWINFLOW, a simple yet effective one-step generative training framework built on novel twin twin-trajectory concept. By extending the standard time interval from t [0, to & [-1,1], 1]. we conceptualize two trajectories originating from the noise distribution. The positive branch (f > 0) maps noise to real data while the negative branch (t < O) maps the same noise to "fake" data, enabling simultaneous learning of both transformations. Our learning objective minimize the discrepancy between the velocity fields of these two trajectories (see Fig. 2a). This forces the model to learn a more robust and direct mapping from noise to data, thereby enhancing 1-step generation performance in self supervied manner. As highlighted in Tab. 1, key advantage of TWINFLOW its simplicity, as it requires no auxiliary trained networks or frozen teacher models. Extensive experiments at different scales demonstrate the effectiveness of TWINFL.WW including
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Table 1: Comparison of different few-step generative modeling methods on their minimal dependence auxiliary trained model and frozen teacher model, Prior 1-step/fcw-step methods such as GAN requires a trained discriminator, diifussion/cosiisennyy distillation' requires frozen teacher model, DMD requires training auxiliary score function for fake data and frozen teacher model, DMD2' trains GAN discriminator and lake score function the same time. Our TWINFLOW achieves 1-step generation without depending on auxiliary trained or frozen models, offering high simplicity.
#Auxiliary trained model
#Foozen teacher model
Method
Generation type
GAN (Goodfellow al., 2014)
1-step
Diffusion models (Ho et al., 2020)
mulli-step
Flow matching models (Lipman et 2022)
multi-step
Diffusion distillation (Salimans Ho, 2022)
few-step
Consistency truining & distillation (Song et al., 2023)
1-step. lew-step
0
0,1
Distribution matching distillation (Yin et al., 2024b;a)
1-step, few-step
1,2
TWINFLOW (Ours)
1-step, few-step
0
Fake Data
Noise
Real Data
Twins flow
>DDGB
OOM
78GB
bss1
676B
6008
bom1
Fo +0l
ba-32
boF32
DMD2
SANA Sprint
TwinFlow Ower- Image-208
SANA 1.68
TwinFlow
Image-208
Qwen-
(a) TWINFLOW overview. The standard flow is the right side (solid lines), its twin (dashed lines) on the left side. The core of our method is to minimize of
Image-208
(b) GPU memory comparison. Directly adopting DMD2 and SANA-Sprint suffers from OOM when ap- the difference between the velocity fields (Av) of the plying to ultra-large models. Our method can be easily standard flow and its twin flow.
applied to train Qwen- Image-20B.
Figure 2: Overview of our TWINFLOW and training GPU memory comparison. The GPU memory usage is measured x024x 1024 resolution Qwen-Image-20B (LoRA tuning) and SANA-1.6B.
text-to-image generation (cf. Sec, 4.2 Sec. 4.3) on large models like Qwen-Image-200 (Wu et al., 2025a). 2-NFE visualizations
Image- 20B are given in Fig. 1. Our key contributions are: (a) Simple yet effective 1-step generation framework. We propose a one-step generation frame- work that does not need auxiliary trained models (GAN discriminators) frozen teacher models (different/consistencyy distillation), thereby eliminating GPU memory cost, allowing for more flexible and scalable training on large models.
(b) Strong 1-NFE performance on text-to-image task. Built on the any-step framework, TWIN- FLOW achieves strong text-to-image performance with only 1-NFE, achieving 0.83 GenEval score, surpassing SANA-Sprint (0.72) and RCGM (0.80).
(c) Effective application on large models. By applying TWINFLOW, we successfully bring 1/2-NFE generation capabilities to Qwen-Image-2005 We achieve GenEval score of 0.86 and DPG score of 86.52 (I-NFE); GenEval 0.87 and DPG Score 87.64 (2-NFE), which are highly competitive with the original 100-NFE scores of 0.87 and 88.32.
2 PRELIMINARIES
Given dataset D, let p(x) represent its data distribution and p(x c) the conditional distribution given a condition c. Generative models aim learn transformation from simple source distribution, p(z), such as the standard Gaussian distribution AO I), to the complex target distribution, p(x).
Any-step generative model framework. A recent framework, RCGM (Sun & Lin, 2025), intro- duces unified formulation for the any-step generation framework, covering paradigms like multi-step generative models (Ho et al., 2020; Song et al., 2020b; Lipman et al., 2022) and few-step generative models (Song et al., 2023: Lu & Song. 2024; Frans et al., 2024; Geng et al., 2025; Sun et al., 2025).
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In this framework, prediction function can be generally defined as f(x,,r) := x,, X which predicts the target point x, from the current point x, along specifie PF-ODE trajectory. The unified training objective for any-step models is given by:
(dxx
dx: 1 At
N E Σ So -x (exn:) (x)
So(x),NNH))
( base
R (t
N+1
[
d
dt
(I)
where X, = a(()z + 7()x, 2 I). function. Under flow matching objective and linear transport, we have fx( r)) where Fe is neural network, F is the no grad version. In practice, we use Network (x_t, E, Σ) as the implementation of Fe (x,,r) r). Equation (I) demonstrates how both multi-step and few-step frameworks can be seen specific instances of the broader any-step framework, which we will detail below.
U),, 4 ~ U(4--1,),, and :( is metric
Multi-step generative models. Diffusion (Ho et al., 2020; Song ct al., 2020b) and flow matching models (Lipman et al., 2022) can be derived from the RCGM framework. By setting N 0, the objective in (1) reduces to their respective training objectives, where the predict function becomes fxx, Af) in the limit At 0. During sampling, these models iteratively solve the PF-ODE by integrating the velocity field dx: starting from noise sample x, p(z) at 1 and ending at dr t == O) to obtain samples from p(x)-
Few-step generative models. Few-step models are also instances of the RCGM framework, typ- ically corresponding to N 1 case: (1) setting t1
At, At + and t2 recovers the objective for consistency models (Song et al., 2023; Lu & Song, 2024); (2) setting t (f2, t) corresponds to shorteut models (Frans et al., 2024), where the predict function is defined as r(),,r) < 8(),,) + 8(),,)) and = 12 E [0, 4); (3) setting 4 = = At (with At 0) yields the McanFlow objective (Geng et al., 2025).
In summary, the RCGM framework offers a unified perspective that integrates both multi-step and few- step paradigms, facilitating their analysis and application. See more related work discussion in Sec. B.
METHODOLOGY
Current few-step methods within the any-step framework (Sec. 2) struggle to achieve high-quality one-step generation without resorting to a GAN loss, which adds significant complexity. To solve this, we propose TWINFLOW, simple and self-contained approach that enhances one-step performance directly within the any-step flow matching framework. Our key idea is the introduction of twin trajectories, which create an internal self-adversarial signal and thus eliminate the need for an external GAN loss (Sec. 3.1). The method works by minimizing a difference between a "fake" and a "real" velocity field, which should ideally be zero (Sec. 3.2). We conclude by demonstrating how integrate TWINFLOW into the broader sty-step framework and provide practical designs in Sec. 3.3.
3.1 TWIN TRAJECTORY FOR SELF-ADVERSARIAL TRAINING
key innovation of our method is the introduction of twin trajectories, which feature time-steps symmetric around f 0 (see Fig. 2a). This structure creates self-contained, discriminator-free adversarial objective designed to directly enhance one-step generation performance.
Creating self-adversarial objective. The standard learning process operates on a time interval t E [o, 1]: real data x is perturbed by x (z))z + (())x, where z A/ I), ~ U(0. 1). To create our self-adversarial objective (as well as the twin trajectories), we extend this time interval from & [0, 1] to E [-1, 1]. The negative half of this interval, E [-1, 0], designated for learning generative path from noise to "fake" data produced by the model itself.
Specifically, task the network learn the generative path to its own outputs. We take fake sample fake generated by the model, i.e. x fake used in implementation, i.e. lake
X; = xtea] Fo (xteell .r) - (L = 1,r = 0 are Fa (z, 0)), and construct corresponding "fake trajectory". in which its perturbed version is defined as xffkee = a(t')zzfake + >('')x fake z fake ~ NO, I), and UO, 1). Here z fake is a different noise, which does not need to the same as z. The network is then trained with the following flow matching objective this trajectory, using negative time inputs -l' E [-1,0] 0]:
L(O) adv Eegniin [d (Fo -('), gake x xfake)]
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where -(.,-) is metric function. Minimizing this loss teaches the network to learn the negative time condition and the transformation from noise to fake data distribution, setting the stage for the rectification loss described in the next section.
3.2 RECTIFYING REAL TRAJECTORY VIA VELOCITY MATCHING
Ideally, we want the twin trajectories to match with each other. As established in Sec. 3.1, the distributions Piake and Preal correspond to trajectories parameterized by the negative and positive time intervals, respectively. Inspired by DMD (Yin et al., 2024b), we can treat this as a distribution matching problem. For any perturbed sample Xt, we aim to minimize the KI. divergence between these two distributions:
DKL (Ptake |Peea)) Ex,,x, log
Plake (xc;) plassl(x))
]]
Ex xt, [- (log (xx) log Plake (x:)) (3)
Preat (Xa)
Velocity matching as distribution matching.
Taking the gradient of (3), we derive:
V DpDaa(Piaellln = VelEx,.
[log Piata((e) - log Preat (x=]]
O blg Plazo(X))
0x:
θ Olog Preal (x+)
dx;
Ex..
Ox.
00
ix.
00
Ox.
Exix
Vxx log Piake (x+) Vx. Preat
(4)
00
Stalee (x1) Sreal(x.)
where s(-) is the score of the respective distribution. The relationship between the score and the velocity field Fo under linear transport (ai() = t, 7tt)
= 1 - t) is given by (see proof in App. D.I):
s(x:) X. + (t)
Fx(x.,t) (f())FFxx..)
Xt
t) Fxi,,)t)
(5)
a(1)
Substituting this relationship from (5) into the KL gradient (4) yields:
+ (1 f) Fa(x,, -t) (- Xi + (1 t) Fo((x..)
0x:
No DEL = Ex.x.
t
30
(1 (1-1) 1)
0x: (xr,()
Exx.xi
Fo(xi,
Fo Fx t)
(6)
Vinke (Xi,
Vrant
where the model is conditioned on -t for the fake trajectory and on for the real one. For simplicity, we denote this velocity difference (see Fig. 2a) as:
Av(xi) = (xe,) (xt -t).
(7)
This derivation recasts the original distribution matching problem into a more practical velocity matching problem. We now show how to formulate this into tractable rectification loss below.
Rectification loss derivation. To derive the rectification loss, first instantiate the gra- dient (6) using the setup in Sec. 3.1. this setting, the network's prediction X; serves as the clean example, and consequently, the perturbed variable x, in (6) corresponds the fake sample xfake, The velocity difference (x;), defined in (7) therefore instantiated as (x(fo))) = reaal (xfr fake ,t') Viake (x/ -t').
Under this setup, the Jacobian term in (6) instantiated as 00
and simplified to:
lake Ha((t)z tnkn + + )x lnkey H(a(t)) fake + ((t))x)))
FF (x("" r) t=1.r=0
FF 0)
00
00
00
00
00
(8)
The KI. gradient in (6) thus takes the form of expectation over the inner product OFa - (xfake)) ae To construct a tractable loss that produces this gradient structure, we employ the stop-gr noopggadieett operator, sg(-). This motivates the following rectification loss:
= Ex, gfaks,r' (F Fo (z. 0), sg (xfob)))) + Fo 0)))
where d(-, .) is metric function. Minimizing Crectity encourages the model straighten the genera- tive trajectories from noise to the data distribution. This rectification allows the entire integration process to be accurately approximated with large step sizes, enabling few-step 1-step generation.
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3.3 THE TWINFLOW OBJECTIVE WITH PRACTICAL DESIGNS
Integration with the any-step framework. Our method TWINFLOW trains a single model to excel at both multi-step and stw-step generation. This is achieved by combining two complementary
objectives with conflicting demands:
The self-adversarial loss (C(0)aar adv in (2)) promotes high-fidelity, multi-step generation by extend-
ing the training dynamics to the interval t [-1,0], 0].
The rectification loss (eecttyy in (9)) optimizes for few-step efficiency by directly straightening the noise-to-data trajectory, enabling rapid, high-quality synthesis.
This creates dual objective: the model must be both precise multi-step sampler and an efficient few-step generator. This leads to application of the any-step framework introduced in Sec. 2, which unifies the demands of (2) and (9). We adopt N formulation of (1) to enhance the training stability.
Our final loss combines the base objective with our proposed terms, which we collectively name ())) TwinFlow The overall loss function in our methodology can be expressed as:
(() [()) buoe + (C(0) Juty + [(0) = L(0) L(0) (win))inlloo
(10)
Practical implementation of mixed loss. The L(O) base and ( TwinFlow objectives in L()) im-
pose different requirements on the target time under the any-step formulation. Specifically, C() base requires r to be sampled from [0. 1J. whereas [(0) TwinFoi necessitates a fixed target time of r = 0.
To accommodate both within single training step. we partition each mini-batch into two subsets.
balancing hyperparameter controls the relative size of these subsets. One portion of the batch is used to compute TwinFlww with O, while the remainder is used for ((s) with a randomly sampled r E [0, 1]. The value of À thus balances the influence of the two losses on the gradient updates. Setting À = 0 disables the C(0)TwinFlow term, while larger values increase its contribution. An ablation study on the impact of À available in Fig. 4a.
EXPERIMENTS
We demonstrate the effectiveness of our method, TWINFLOW, on two fronts. First, we highlight its versatility and scalability, apply TWINFL.WW
unified multi-modal models, e.g. Qwen-Image- 20B (Wu et al., 2025a), as shown in Tab. 2. Second, we benchmark it against state-of-the-art (SOTA) dedicated texi-to-image models, with results presented in Tab. 4.
4.1 EXPERIMENTAL SETUP
This section details the experimental setup and evaluation protocol of our proposed methodology. Image generation on multimodal generative models. We conduct evaluations on unified multi- modal models (i.e. takes both texts and images conditions and capable of generating texts and images). (f) Network architectures: We conducted LoRA (Hu et al., 2022) (Tab. 2) and Qwen-Image. We also do full hull-parameter training experiments on OpenUni-512 (Wu et al., 2025c). (2) Benchmarks: Following recent works (Pan et al., 2025; Chen et al., 2025b; Deng et al., 2025; Wu et al., 2025a), text-to-image generation tasks. For text-to-image generation, we use GenEval, DPG-Bende (Hu al., 2024), and WISE (Niu et al., 2025). Other training settings are detailed in App. C.I. Text-to-image generation. For text-to-image generation, we evaluate on dedicated text-to-image models (i.e. primarily takes texts condition and only generating images). (1) Network architec- tures: We use M.NN--066/1..6 (Xic et al., 2024a) in our experiments. (2) Benchmarks: Following SANA-series (Xie al., 2024a; 2025a), use GenEval (Ghosh et al., 2023) and DPG-Bench (Hu et al., 2024) as evaluation metrics. Other training settings are detailed in App. C.2.
full-parameter training (Tab. 3) of TWINFLOW
benchmarks in
4.2 IMAGE GENERATION ON MULTIMODAL GENERATIVE MODELS
We demonstrate TWINFLOW's scalability by achieving competitive I-NFE text-to-image generation on the 20B parameter Owen-Image series (Wu al., 2025a). This breakthrough addresses a critical gap in the field, as prior few-step approaches are rarely applied on models exceeding 3B parameters due to instability in GAN-based loss scale.
Our approach offers two key advantages over otate-of-the-art unified multimodal generative models: (a) TWINFLOW maintains >0.86 GenEval score at 1-NFE on Owen-Image-2001 surpassing most multi-step models (40-100 NFEs), e.g. Bagel (Deng et al., 2025), MetaQuery (Pan et al., 2025).

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Table 2: System-level comparison of TWINFL.OW with unified multimodal models efficiency and performance on text-to-image tasks. The best and second best results of 1-NFE and 2-NFE are highlighted. means using L.M rewritten prompts for GenEval. wwwe--maag--TTNiLLL in this table is under LoR A training. Owen-Imaae-LLie GenEval and DPG-Bench.
generates almost identical images for the prompt when evaluating
Image Generation
Method
NFE 1
GenEval
DPG-Bench
WISE
Chamelcon (Team, 2024)
0.39
SEED-X (Ge al., 2024)
50
0.49
Show-o (Xie et al.. 2024b)
50x2
0.68
67.27
0.35
Janus-Pro (Chen et al., 2025c)
0.80
84.19
0.35
MetaQuery-XI (Pan al., 2025)
30
0.78 / 0.80
81.10
0.55
BLIP3-o-8B (Chen al., 2025b)
30x2 50x2
0.84
81.60
0.62
Uni UniWorld-V1 (Lin ct al., 2025)
28x2
0.80 0.884
0.55
OpenUni-L-512 (W al., 2025c)
20x2
0.85
81.54
0.52
Bagel (Deng et al., 2025)
50x2
0.82 0.888
0.52
Show-o2-7B (Xie et al.. 2025b)
50x2
0.76
86.14
0.39
OmniGen (Xiao ct al., 2024)
50x2
0.70
81.16
OmniGen2 (Wu et al., 2025b)
50x2
0.80 / 0.86"
83.57
Qwen-Image (Wu et al., 2025a)
50x2
0.87 0.9.1 RL
88.32
0.62
owen-Image-Lighminn (ModeITC, 2025)
0.85
87.79
0.51
OpenUni- MinnUni-RCGM-SI11 -12 (Sun & Lin, 2025)
2
0.85
80.15
0.50
OpenUni-RCCGM-512 (Sun & Lin, 2025)
0.80
76.40
0.45
OpenUniiTwwNNF..owW5111 (Ours)
0.85
79.82
0.50
0.83
79.07
0.48
Owen-Image Image-Maag- (Sun & Lin, 2025)
2
0.82
84.09
0.50
Qwen-Image-RCGG (Sun & Lin, 2025)
0.52
59.50
0.30
0.87 / 0.91
87.64
0.57
Qwen-Inage--TWINFLov (Ours)
0.86 / 0.90*
86.52
0.54
(b) TWINFLOW achieves this without auxiliary networks, unlike competing few-step methods that require distillation or specialized training pipelines (Yin et al., 2024b;a).
We evaluate the ext-to-image generation capabilities of lween-mage--e TWWNFLOW on several standard benchmarks: GenEval (Ghosh et al., 2023), DPG-Bench (Hu et al., 2024), and WISE (Niu et al., 2025). Our model demonstrates strong performance across all benchmarks with only 1-NFE, achieving results that are both competitive and efficient. Detailed results are provided in App. C.I.
Evaluation on text-to-image benchmarks. As shown in Tab. 2, Qwen-Image-TWiNEE score of 0.86 on GenEval and 86.52% on DPG-Bench with just 1-NFE, closely matching the original model's 's performance at 100-NFE. Compared to Twen--mage--ieee distilled model, our model surpasses it on GenEval and WISE with only 1-NFE. Furthermore, our model outperforms Imen-Image- Saan--lmaa---GMMMM RCGM (Sun & Lin, 2025) on both GenEval and DPG- JPG-Bench under 1-NFE and 2-NFE settings, with notable improvements of 0.34* on GenEval, 27. 77.0% on DPG-Bench, and 0.255 on WISE under the 1-NFE setting.
achieves
(ModelTC, 2025), a 4-step
We also benchmark Qwen-Image-TTwnee TWINFLOW against other prominent multi-stop unified multimodal generative models, such as MetaQuery-XL (Pan et al., 2025), BLIP3-o-SB (Chen et al., 2025b), and Bagel (Deng et al., 2025). Our model consistently surpasses these baselines with or 2-NFE across all evaluation metrics. Beyond Qwen-Image we also apply TWINFLOW et al., 2025c), achieving GenEval of 0,80 and DPG-Bench of 76.40 under the I-NFE setting, which is also close to its original performance. These findings underscore the versatility and effectiveness of TWINFLOW across different architectures and scales.
OpenUni (Wu
Further exploration on 20B full-parameter training on Qwen-Image. Tab. 3 demonstrates the scalability and performance advantages of TWINFLOW on the large-scale Qwen-Imaag-200B Existing distribution matching methods, such as VSD (Wang et al., 2023), DMD (Yin et al., 2024b), and SiD (Zhou et al., 2024), typically require maintaining three separate model copies (generator, real score, and fake score), ) leading to significant memory overhead. In contrast, TWINFLOW distinguishes itself through unified design:
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Table 3: Comparison full-parameter training efficiency and performance between TWINFL.OW and baselines on text-to-image tasks using Qwen-Image 20B. The raw setting denotes that the generator, real score, and fake score are instantiated as separate models. Though using FSDD-v2,, this configuration still leads OOM. Therefore, for VSD, SiD, and DMD, the fake score implemented using LoRA (r 64) memory feasibility, indicates severe diversity degradation (mode collapse), characterized by nearly identical outputs on GenEval and DPG-Bench. For sCM and MeanFlow, the JVP is approximated via finite difference.
Image Generation DPG-Bench
Method
NFE +
GenEval
WISE
Qwen-Image (Wu et al., 2025a)
50x2
0.87 0.91
88.32
0.62
VSD (Wang et al., 2023) (raw)
OOM OOM OOM
OOM
OOM
DMD (Yin al., 2024b) (raw)
OOM
OOM
SiD (Zhou et al., 2024) (raw)
OOM
OOM
1
0.67
84.4
0.22
VSD (Wang et al., 2023)
2
0.73
86.16
0.34
1
0.81
84.31
0.47
DMD* (Yin et al., 2024b)
2
0.80
84.08
0.46
1
0.77
87.05
0,42
SiD (Zhou al., 2024)
2
0.78
86.94
0.41
1
0.55
79.12
0.21
sCM (Lu & Song, 2024) (JVP-free)
2
0.64
83.91
0.46
1
0.49
82.39
0.33
MeanFlow (Geng al., 2025) (JVP-free)
2
0.57
85.09
0.40
1
0.56
76.15
0.31
RCGM (Sun Lin, 2025)
2
0.78
85.01
0.50
1 0.85
85.44
0.51
Ours
2
0.86
86.35
0.55
1 0.89
87.54
0.57
Ours (longer training)
2
0.90
87.80
0.59
NFE=1
NFE=4
NFE=8
NFE=16
NFE=32
NFE=1
NFE=2
Qwen-lmage-222 (cfg=4.0)
Ours (No cfg)
Figure 3: Visualization of images generated by Qwen-Image and Qwem- Dweem-maaee- Image-TWINFL... TWINFLTWWWWWWW w.rt. NFEs. Dwve--Iage--TiNNLLOO. is capable of generating high-quality images with just NFE, which better than the original Qwen-Image's performance 16 NFEs. Furthermore, when comparing 2-NFE results to 32-NFE outputs of Qwen-Image, our method demonstrates better visual details. See prompts in App. E.3.
(a) Simplicity and efficiency: By integrating the generator, real/fake score estimation into single model, TWINFLOW eliminates the need for redundant parameters. This allows for full hall-parameter training at the 20B scale.
(b) Performance superiority: With this unified design, TWINFLOW outperforms all baselines on Dwve-Immae-2001 Notably, it achieves superior generation quality with just 1-2 NFE compared to sCM (Lu & Song, 2024) and MeanFlow (Geng al., 2025).
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Published as a conference paper at ICLR 2026
Table System-level comparison TWINFLOW with text-to-image models in efficiency and performance. Throughput (batch=10) and latency (batch=l) are benchmarked on single A100 with BF16 precision. The best and second best results across 1-NFE are highlighted. means results tested by ourselves.
Throughput (sampleshs Pretrained multi-stop models
Method
NFE
Latency (8) 4
#Params
GenEval f DPG-Bench f
SDXL (Podell 2023)
50x2
0.15
6.5
2.6B
0.55
74.7
PixAnt-E (Chen al., 2024a)
20x
0.40
2.7
0.6B
0.54
80.5
SD3-Medium [Esser al.. 2024b)
28x2
0.28
4,4
2.0B
0.62
84.1
FLUX-Dev (Labs, 2024)
50x2
0.04
23.0
12.0B
0.67
84.0
Playground (1.iu al., 2024)
0.06
15.0
24B
0.76
87.0
SANA-0.6B (Xie al.. 2024a)
20x2
1.7
0.9
0.6B
0.64
83.6
SANA-1.6B
20x2
SANA-1 55 (Xie et 2025a)
20x2
0.26
4.2
4.8B
0.81
84.7
Ammin--naae-2.00
18x2
2.6B
0.73
87.2
Few-step models (training w/ auxiliary models)
SDXL-DMD2 Yinn et al., 2024a)
2
2.89
0.40
0.9B
0.58
FLUX-Schnell (Labs, 2024)
2
0.92
1.15
12.0B
0.71
SANA- SpriSt-p.ttttte 0.6B (Chen et al. 2025c)
2
6.46
0.25
0.6B
0.76
81,51
SANA Sprint- .68 (Chen et al., 2025c)
2
5.68
0.24
1.6B
0.77
82.11
PiXAn-DMD (Chen et al., 2024a)
4.26
0.25
0.45
SDXL-DMD2 (Yin et al., 2024a)
3.36
0.32
0,9B
0.59
FLUX Schnell (Labs, 2024)
1.58
0.68
12.0B
0.69
SANA--Srrnt-O 6B (Chen al., 2025c)
7.22
0.21
0.6B
0.72
78.6
SANA-Sprinte 66 (Chen et al. 2025c)
6.71
0.21
1.6B
0.76
80.1!
Few-step models (training w/o auxiliary models)
SDXL-LCM (Luo
2023)
2.89
0.40
PixArt-LCM (Chen ct al., 2024b)
3.52
0.31
0.6B
0.42
PCM (Wang et al., 2024a)
2.62
0.56
0.9B
0.55
SD3.5-Turbo (Esser et al., 2024a)
RCGM-0.6B (Sun Lin, 2025)
6,50
0.26
0.6B
0.85
80.3
RCGM 1.68 (Sun Lin, 2025)
5.71
0.25
1.6B
0.84
79.1
TWINFLOW-0.68 (Ours) TWINFLOw-1.68 (Ours)
3
6.50
0.26
0.6B
0.84
79.7
5.71
0.25
1.6B
0.83
79.6
SDXL-LCM (Luo al., 2023)
3.36
0.32
0.9B
0.28
PixAn-LCM (Chen et 2024b)
4.26
PCM (Wang et al., 2024a)
3.16
0.40
0.9B
0.42
SD3.5-Turbo (Esser al., 2024a)
2.48
0.45
8.0B
0.51
RCGM-0.6B (Sun Lin, 2025)
7.30
0.23
0.6B
0.80
77.2
RCGM-1 1.68 (Sun & Lin, 2025)
6.75
0.22
1,6B
0.78
76.5
TiM (Wang et
2025)
0.8B
0.67
75.0
TWINFLOW-0.6B (Ours)
7.30
0.23
0.6B
0.83
78.9
TWINFLOW--166 (Ours)
0.22
79.1
Discussion on open-source community efforts. To the best of our knowledge, Dwen-Immaee Lightning (ModeITC, 2025) is the only open-source few-step model on large models. It is developed using DMD2 (Yin et al., 2024a) but removing GAN loss. This also indirectly reflects the high cost associated with using GAN loss. However, severe mode collapse: when given the same prompt but different noise inputs, the generated images remain nearly identical across runs. This lack of diversity is empirically demonstrated in the visual comparisons provided in App. E.
observe that owen-Image-Ligheee
suffers from
Exploration on image editing. We also conducted preliminary exploration of our TWINFLOW'S capabilities in image editing using a small tuning dataset of approximately 15K editing pairs. Despite the limited scale, our results (see Tab. 8) demonstrate that TWINFLOW can convert Qwen-Image Edit (Wu et al., 2025a) into 4-NFE editing model. This suggests that with access to more diverse editing datasets, anticipate further improvements in both fidelity and versatility of edited outputs.
4.3 IMAGE GENERATION ON DEDICATED TEXT-TO-IMAGE MODELS
To validate our method's versatility, we also benchmark on traditional text-to-image generation. As shown in Tab. 4, we first benchmark against pretrained multi-step models (typically requiring >40- NFE). Following the categorization in Tab. 1, we compare against SOTA few-step models, grouped by their reliance auxiliary components: those trained with versus without auxiliary models. Critically, parameter tuning on SANA-0.6B/1.68 backbones enables fighfiideitty image generation in just 1-2 NFE.

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Published as a conference paper at ICLR 2026
6:731
0.925
01,8.77
0.354
0.871
0.65
84
0.834
03664
DPGScooe
DPC Score
0.80
NPE
0.789
0,831
0.663
0.874
0.75
0.817
08773
0.872
78
L-NFE L-NEY
0.676
0.769
0.444
8,866
0.70
6-880
Open
SANA Models
Qwen-Image
Training Step
(a) Influence of X.
(b) Impact of TiwnFlow
(c) Training steps NFE.
Figure 4: Ablation studies of TWINFLOW. Ablation presented in (a) and (c) conducted on Qwen-Imager
TWINFLOW. Results shown in (b) trained on the same datasct but with different models.
(a) 1-NFE setting:
The efficacy of our method is particularly pronounced in the more demanding 1-NFE inference setting. Here, our models (0.6B: 0.83, 1.6B: 0.81 11 on GenEval) significantly outperform other leading I-NFE methods, such as SANA- SANA-Sprint (0.76) (Chen et al., 2025c), FLUX-Schnell (0.69) (Labs, 2024), and SDXL-DMD2 (0.59) (Yin et al., 2024a). Notably, our 1-NFE TWINFLOW-ON -066066 (GenEval: 0.83) exceeds the generation quality of the 40-NFE SANA-1.5-4.88 (Xie et al., 2025a) model while offering substantially greater computational efficiency.
(0.78) (Sun & Lin, 2025),
(b) 2-NFE setting: TWINFLOW-O.. -066B achieves a throughput of 6.50 samples/s and latency of 0,26s, performance metrics comparable to the originally reported SANA values. On the GenEval benchmark, our model attains a score of 0.84, surpassing not only the SANA-Sprint series (0.76 and 0.77) but also powerful multi-step models like SANA-1.5 (0.81) and Playground v3 (0.76). Our models also demonstrate competitive performance on DPG-Bench, with scores of 79.7 for the 0.6B variant and 79.6 for the 1.6B variant.
1WINFLOW-0.6B/ BBB..BBBBBB....B.BB achieves SoTA text-to-image generation performance the GenEval bench- mark using just I-NFE, surpassing both SANA-Sprint and RCGM. While we slightly underperform on DPG-Bench relative to SANA-Sprint due to SANA-Sprint's reliance extensive, proprietary training data. We believe this gap is primarily data-driven and can be effectively closed by training on larger, higher-quality datasets.
4.4 ABLATION STUDY AND ANALYSIS
Influence of X. As described in Sec. 3.3, À is designed control the sample distribution of Lasse and LTwinPlow- In Fig. 4a, we visualize the DPG-Bench performance w.r.t. À at 1-NFE and 2-NFE, We observed that as increases from 0, the performance on DPG-Bench initially increases and then decreases, reaching its peak at approximately X 1/3. These results indicate that appropriately balancing samples in the local batch helps improve the model performance.
Impact of LTwinFlow on different models. We conduct an ablation study to analyze the impact on text-to-image performance of using LTwinFlow on different models. As illustrated in Fig. 4b, incorporating WiinFlow significantly enhances performance: it improves 1-NFE performance for the text-to-image task across OpenUni, SANA, and especially
wwnneImmaee (from 59.50 to 86.52).
Effect of training steps vs. NFE. As illustrated in Fig. 4c, the experimental results demonstrate that as training progresses, the "comfort regime" for optimal sampling steps shifts accordingly. Notably, GenEval performance improvements are observed across both 1-step and few-step scenarios, with significant gains achieved as training progresses, which shows the effectiveness of LTwinFlow-
5 CONCLUSION AND LIMITATIONS
We introduce TWINFLOW, a streamlined framework for training large-scale, few-step continuous generative models. Unlike complex DMD-seriee approaches, method eliminates auxiliary compo- nents like GAN discriminators or frozen teacher models, enabling straightforward and efficient 1-step training. While extensive experiments confirm its high-quality text-to-image synthesis, two primary limitations remain: its scalability to tasks like image editing is unexplored, and its performance across diverse modalities like video and audio requires further validation. Addressing these gaps will be key to developing more versatile generative models.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENT
This work was supported in part by the National Science and Technology Major Project (No. 1022D00111001, NSFC under No. 62576285, the Research Center for Industries of the Future (RCIF) at Westlake University, and the Westlake Education Foundation.
We thank the anonymous reviewers for their insightful comments and suggestions, which greatly enhanced the quality this paper. We also extend our gratitude to Deyuan Liu, Chuyan Chen, and Jun Xie for their helpful discussions and feedback.
ETHICS STATEMENT
Our work is conducted in full alignment with the ICLR Code of Ethics, and we are committed to upholding the principles of transparent and responsible research. This study did not involve human participants or the use of personal sensitive data, thereby negating the need for an institutional ethics review. All datasets utilized are publicly available under their respective licenses, and we have provided appropriate attribution to their original sources. To foster transparency and enable further innovation, our implementation code and experimental configurations will be made available. We also affirm that conflicts of interest external funding have influenced this work.
REPRODUCIBILITY STATEMENT
To ensure that our findings can be accurately and transparently replicated, have provided comprehensive account of our experimental methodology. Exhaustive details concerning the model architectures and evaluation protocols documented in Sec. 4 of the main text and further elaborated in App. C. Following the acceptance of this paper, we will make our entire source code publicly available to facilitate verification and future research.
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CONTENTS
1 Introduction
2 Preliminaries
3 Methodology
3.1
Twin Trajectory for Self-adversarial Training
3.2
Rectifying Real Trajectory via Veloeity Matching
3.3
The TwinFlow Objective with Practical Designs
Experiments
4.1
Experimental Setup
4.2
Image Generation on Multimodal Generative Models
4.3
Image Generation on Dedicated Text-to-image Models
4.4
Ablation Study and Analysis
10
5
Conclusion and Limitations
10
6 Ethics Statement
11
7 Reproducibility Statement
11
A Use of LLMs
17
Related Work
17
Detailed Experiments
18
C.1 Detailed Implementation on Multimodal Generative Models
18
C.2 Detailed Implementation on Dedicated Texa-to-image Models
18
C.3
Exploration Image Editing.
19
D Theoretical Analysis
19
D.I
Transformation from Score Velocity
19
E
Visualization Results
20
E.1
Comparison of Owen-Image-TWye Teee-TIING TWINFLOW and Qwen- Imgge--gtt-i-nnngg
20
E.2
Visualization Results across Training Steps
20
E.3
Selected Prompts Used for Visualization
22
E,4
High Resolution Visualization
22
E.5
Fake Trajectory Visualization
22
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A USE OF LLMS
This paper only uses LLMs for polishing.
B RELATED WORK
Multi-step generative methods. Diffusion (Ho et al., 2020; Dhariwal Nichol, 2021) and flow matching (Lipman et al., 2022) models have shown impressive performance in image generation. They progressively transport a simple noise distribution to the data distribution, either by reversing noising process integrating probability- ODE. However, this iterative nature introduces bottleneck in inference efficiency, as generating an image requires numerous sequential evaluation steps.
Few-step generative methods. Various methods tend accelerate the sampling process, such as diffusion distillation (Luhman & Luhman, 2021; Salimans & Ho, 2022) and consistency distilla- tion (Song et al., 2023; Song & Dhariwal, 2023). They typically train a student model to approximate the ODE sampling trajectory of a frozen teacher model in fewer sampling steps. While effective at moderate NFEs, these approaches depend on a frozen teacher, and quality often drops sharply in the extreme few-step regime (< 4 NFEs). Incorporating GAN- like loss into distillation (e.g., CTM (Kim et al., 2023), ADD/LADD (Sauer et al., 2024b;a), DMD/DMD2 (Yin et al., 2024b;a)) can improve sharpness and alignment at few steps. Yet these frameworks increase training complexity and insta- bility: they typically introduce auxiliary modules (discriminators, fake-sample score functions) and still rely on frozen teacher, leading to higher memory overhead and sensitivity to hyperparameters. For latre models, this added complexity often translates to out-of-memory failures or brittle training dynamics.
Few-step applications in large generative models. The tension between speed and quality is amplified in large-scale systems. For instance, Qwen-Imaae-20B (Wu et al., 2025a) typically requires 100 NFEs, leading to substantial latency (~^40s on a single A100 for 1024x 1024 resolution). Recent works distill cut NFEs while preserving compositionality and aesthetics: LCM-style distillation for latent models (Luo et al., 2023), large-scale text-to-image distillation pipelines (e.g.. Aix Delta (Chen al., 2024b), SDXL distillation (Lin et al., 2024), -cheelchnnel (Labs, 2024)), and brid frameworks such as SANA-Sprint (Chen et al., 2025c) that combine teacher guidance with adversarial signals. Recently, Hunyuan Image-22- (Team, 2025) explore McanFlow (Geng et al.. 2025) for mid-step acceleration (16 NFEs). Nevertheless, when targeting 1-2 NFEs on 1024x x 1024 text-to-image with 10B-20B backbones, these pipelines face practical barriers: dependence on frozen teachers, extra discriminators score networks, unstable adversarial training, and prohibitive memory costs that hinder straightforward scaling.
Concurrent progress in few-step generative models. Recent research has significantly diversified the landscape of efficient generative models, introducing novel training objectives and distillation paradigms. FACM (Peng et al., 2025) stabilizes consistency training by explicitly anchoring the shortcut objective to the instantaneous velocity field of a flow matching task. SoFlow (Luo et al., 2025) proposes solution consistency loss that bypasses expensive JVP calculations, enabling efficient one- step generation. Adopting ppticy-optimization perspective, -Flow (Chen et al., 2025a) decouples the number of network evaluations from ODE integration steps via imitation learning framework termed π- --ID. Furthermore, Tong et al. (2025) introduces a data-free distillation paradigm that samples from the prior distribution to climinate teacher-data mismatch, while Self-E (Yu et al., 2025) employs a self-evaluation mechanism to refine generation quality by leveraging the model's 's own estimates. T2I-Distill (Pu et al., 2025) systematically evaluates sCM (Lu & Song, 2024) and MeanFlow (Geng et al., 2025) methods and developed efficient JVP kernels, offering practical guidelines for adapting them to open-domain text-to-image generation.
In the realm of methods with distribution matching, Decoupled-DMi (Liu et al., 2025a) and DMD- R (Jiang et al., 2025) offer new insights into the DMD objective; the former identifies CFG augmenta- tion as the primary driver of acceleration while treating distribution matching as a regularizer, whereas the latter integrates reinforcement learning to enhance mode coverage during distillation. Targeting large-scale video generation, rCM (Zheng et al., 2025) combines continuous time consistency models with score distillation regularization to resolve fine-detail degradation, and Transition Matching Distillation (TMD) (Nie et al., 2026) utilizes a decoupled architecture with a recurrent flow head to efficiently distill video diffusion models into few-step generators.
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C DETAILED EXPERIMENTS
C.I DETAILED IMPLEMENTATION ON MULTIMODAL GENERATIVE MODELS
Training Datasets. For training OpenUni (Wu et al., 2025c) and Qwen-Image (Wu et al., 2025a), used the same datasets as in our text-to- text-to-image experiments but excluded ShareGPT-4o--mage (Chen et al., 2025d). For exploration 2024b) employs regression loss, we need to pre-generate offline data using Qwen-Image. To ensure fair comparison with established baselines, randomly sample 50K prompts from the datasets we used, and generate images using Qwen-Image For Qwen- Laan--maae--aa Edit (Wu et al., 2025a), we used only the part of editing split of the ShareGPT-44- dataset.
full-parameter training
Owen-Image since DMD (Yin et al.,
Training configurations. We performed full-parameter fine-tuning experiments on OpenUni, using an image resolution of 512x512. With a batch size of 128, the model was trained for 600,000 steps. For other training configurations, please refer to Tab. 6.
For LoRA fine-tuning on Qwen-Image and Owen-Image-Edle We the rank (r) and alpha (a) to 64 for Qwen-Imear Image, and to 64 and 32, respectively, for Qwen-Image-Edih This LoRA setup comprises approximately 420M trainable parameters. comprehensive list of training configuration provided in Tab. 6. For full-parameter training Qwen-Image, we add additional time embedder to serve as the target timestep condition.
Additional detailed results GenEval. As detailed in Tab. our Owen-Image-TTWiNiioo
achieves 0,86 with I-NFE. Notably, when using LLM rewritten prompts, our GenEval score comes to 0.90, which very close to Qwen-Image-RL (Wu al., 2025a) (0.91). Significant score increases are observed in the Colors and Attribute Binding subtasks. This indicates that our model exhibits enhanced image generation capabilities when processing long input instructions.
Table 5: Detailed evaluation results on GenEval for text-to-image models. Owen-Image-LLghee are evaluated with I-NFE. "means using LLM rewritten prompts for GenEval.
Single Object
Two Object
Attribute Binding
Model
Counting
Colors
Position
Overall
SEED-X (Ge et al., 2024)
0.97
0.58
0.26
0.80
0.19
0.14
0.49
Emu3-Gen (Wang et al., 2024b)
0.81
0.17
JanusFlow (Ma al., 2025)
0.97
0.59
0.45
0.83
0.53
0.42
0.63
Show-o (Xic al., 2024b)
0.99
0.80
0.66
0.84
0.31
0.50
0.68
OmniGen (Xian ct al., 2024)
0.98
0.84
0.66
0.74
0.40
0.43
0.68
lanus-Pro-7B (Chen et al., 2025e)
0.99
0.89
0.59
0.90
0.79
0.66
0.80
OpenUni-512 (Wu
2025c)
0.75
0.85
Bagel (Deng
2025)
0.99
0.94
0.81
0.88
0.64
0.63
0.82
OmniGen2 (Wu et al. 2025b)
1.00
0.95
0.64
0.88
0.55
0.76
0.80
Show-o2-7B (Xic ct al., 2025b)
1.00
0.87
0,58
0.92
0.52
0.62
0.76
Owen-Image (Wu ct al., 2025a)
0.99
0.92
0.89
0.88
0.76
0.77
0.87
Qwen- wven-Image-Liighnnnng (ModelTC, 2025)
0.99
0.89
0.87
0.75
0.85
OpenUni-TWINFLOOw.t (1-NFE)
0.99
0.91
0.69
0.90
0.79
0.72
0.83
Owen-Image- -wnn-nnagg-TTiNNLooww (I-NFE)
0.84
0,90
0.75
0.74
0.86
Swen-ImageTTWNNF.. (1-NFE)
0.99
0.94
0.87
0.96
0.78
0.83
0.90
C.2 DETAILED IMPLEMENTATION ON DEDICATED TEXT-TO-IMAGE MODELS
Training datasets. Considering training datasets, Echo-4o (w/o multi-reference split) (Ye et al., 2025), and ShareGPT-4o Image (Chen et al., 2025d). Together, these three instruction tuning datasets comprise approximately 200,000 text-to-image samples.
30IP-3-660K (Chen et al., 2025b),
Training configurations. All experiments on SANA-0.6B/L.C backbones are conducted with full- palammerreter tuning. we fine-tuned these two models for 30,000 steps, using batch sizes of 128 and 64 respectively. Other detailed training configurations are provided in Tab. 6.
Additional detailed results on GenEval. We list the detailed results of TWINFLOW-006B/11 on GenEval (Ghosh et al., 2023) along with other SOTA multi-step eoxt-to-mmage models (except FLUX -.hhn-Sh in Tab. 7. demonstrates except the overall score, our WWWNFF.ow-0.6B/1... also
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Table 6: Detailed training configurations for experiments text-to-image models and multimodal generative models.
Configuration
SANA-066
SANA-L6B
OpenUni-512
Owen-Image (LoRA)
Owen-Image (Full)
Owen-Image Edit
Optimizer Settings
Optimizer
RAdam
RAdam
AdamW 1 10
AdamW
AdamW
Adam'V x 100
Leariing Rate Decay (f)
x 10
x 10
10
1 x 10
D (0.9, 0.99)
(0.9, 0.95)
(099 0.95)
0.99)
(0.9, 0.99)
(0.9, 0.99)
Training Details
Batch Size
128 Constant
128
128 60000
64 70X0
24
Training Steps
30000
3000, 10000 (longer training)
7000
Leamiigg Rate Schodaler
Coestant
Constant
Constant
Consiamt
Constant
Gradient Clapping
1.0
1.0
1.0
Random Seed
42
42
42
42
42
42
LoRA
LeRA a
32
PMA Docay Rate
0.99
0.99
0.99
0
0.99
0)
outperforms these multi-step models with 1-NFE in sub tasks such as Position (0.6B: 0.84, 1.6B:
0.79) and Attribute Binding (0.6B: 0.70, 1.6B: 0.68).
Table 7: Detailed evaluation results on GenEval for text-to-image models.
Single Object
Two Object
Attribute Binding
Model
Counting
Colors
Position
Overall
SDXL (Liin et al., 2024)
0.98
0.74
0.39
0.85
0.15
0.23
0.55
PixArt-E (Chen al., 2024a)
0.98
0.59
0.50
0.80
0.10
0.15
0.52
SD3-Medium (Esser al., 2024a)
0.98
0,74
0.63
0.67
0.34
0.36
0.62
FLUX-Dev (Labs, 2024)
0.98
0.81
0.74
0.79
0.22
0.45
0.66
FLUX-Schnell (Labs, 2024)
0.99
0.92
0.73
0.78
0.28
0.54
0.71
SD3.5-Large (Esser et al,, 2024a)
0.98
0.89
0.73
0.83
0.34
0.47
0.71
Lumina-Image-2200 (Qin ct al., 2025)
0.87
0.67
0.62
0.73
SANA-0.6B (Xie et al., 2024a)
0.99
0.76
0.64
0.88
0.18
0.39
0.64
SANA-I.6B (Xie et al., 2024a)
0.99
0.77
0.62
0.88
0.21
0.47
0.66
TWINFLOW-0.68 (I-NFE)
0.98
0.90
0,68
0.89
0.84
0.70
0.83
TWINFLOW-1.6R (I-NFE)
0.99
0.88
0.65
0.86
0.79
0.68
0.81
C.3 EXPLORATION ON IMAGE EDITING
We conducted a preliminary exploration of our method on image editing tasks using a subset of approximately 15,000 square images from Chen et (2025d). We fine-tuned the Awen-Image-Fddee model (Wu et al., 2025a) using LoRA at a fixed 512x512 resolution, with training configurations detailed in Table 6. Note that we use a low and fixed resolution during training: during testing, we use resolution that is the same as the input image size. Our Lree--mmageEEdit- -eee--EiF--TFFLLLL can effectively edit images
to 4 NFES. With NFEs, achieves a score of 3.47 on the ImgEdit, surpassing all multi-step models except Qwen-Image-eeee Image-Edit Edit itself.
Despite the naive usage of the dataset and training strategy, these results highlight the significant potential of our approach. We believe that with further scaling, method can achieve strong performance in a single-step (I-NFE) setting on image editing tasks.
D THEORETICAL ANALYSIS
D.I TRANSFORMATION FROM SCORE TO VELOCITY
In this section, we derive the equation between score and velocity. According to Equation in Sun et al. (2025) with flow matching, we have:
a(t)- Fo(xi, -á(t)
t. Fo X,
()
a(t) . 1(t) - ā(t) ((t)
(t. - t))
1. Fo(xi, 1) Xt.
xt - -FFo(xi,t).
(11)
(-1)
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Table 8: Comparison of TWINFLOW with unified multimodal models in performance image editing tasks. GEdit-Bences SC measures Semantic Consistency, G. G_PQ evaluates Perceptual Quality, and G_O reflects the Overall Score. All metrics are evaluated by GPT-4.1.
Image Editing
Method
NFE
GEdit-EN (Full set)
ImgEdit
G_SC
G_PQ
G_O
UniWorld-Y V1 (Lin et al., 2025)
28x2
4.93
7.43
4.85
3.26
OmniGen (Xiao et al., 2024)
50x2
5.96
5.89
5.06
2.96
OmniGen2 (Wu et al., 2025h)
50x2
7.16
6.77
6,41
3.44
X-Fdt (Liu al., 2025b)
28x2
7.66
7.35
6.97
3.06
Bagel (Deng al., 2025)
50x2
7.36
6.83
6.52
3,20
Owen-Image (Wu et al., 2025a)
50x2
8.00
7.86
7.56
4.27
Oven-Imagg-Fdit-TTTwi Fw Bwee--mage-FFdi--TwiNPP
5.95
6.97
5.91
3,55
2
5.94
6.81
5.85
3.47
According to Theorem 2 in Sun et al. (2025), we have:
(() x, a² (t) xx (x,,
X, 12+tVVx, log
X; + +2x2s(()))
7(()
(1 1)
1
(12)
where s(x,) = Xx, logpt(x;) By simple transposition of s(x:) term, we can get:
X₁ + t sx,,)
1-t X1 - t. F)(x
= (s(x)) (1 - t) (x: Fo (x(xx.))) t)) -x, -xx, + (f F)(((t)
s(x)) -tx, (12 1) Fx
+ F)((x,))
(13)
E VISUALIZATION RESULTS
E.1 COMPARISON OF QWEN-IMAGE- TWINF-LO AND
As Fig. shown, our comparative analysis reveals a notable limitation in Qwen- wwven-Imag-LLighning generation. The model produces images with very low diversity; outputs are often highly similar and visually repetitive even when initialized with different latent noise. Our Lyve-IImag-TTiNNF does not exhibit model collapse, demonstrating the ability to generate a rich variety of high- images. We also quantify the similarity by using LPIPS metric in Tab. 9, We perform the diversity evaluation GenEval. Specifically, for each prompt, we compute the average pairwise LPIPS distance among the 4 generated samples. The final score is the mean of all scores across the entire samples. The results quantify that Qwen-Image -mage-Lightninn shows obvious diversity degradation.
Table 9: Quantification of diversity using LPIPS for Qwen-Image- Lighttigg-Lggiiigngg and Qwen-Image- TWINFLOW. gowen-image-Lighning shows obvious diversity degradation.
Model
NFE
LPIPS
Owen-Imaae-Lightnnn
0.2996
Qwen-Image- Teennne--TiINEE FWOW
0.5044
Owen-Image- Lightnin
2
0.3046
Qwen-Image- Tarage-TWINFL.. FWINFLOW
0.5188
E.2 VISUALIZATION RESULTS ACROSS TRAINING STEPS
As illustrated in Fig. 6, our method exhibits a two-stage training dynamic. Initially (200 to 400 steps), it demonstrates rapid convergence in 1-NFE performance, quickly establishing strong baseline.
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Ours: Remains Diversity
Owen-Image- Lighting: Almost Identicall
Figure 5: Comparison between Qwen-Image- Sean--maa--TWINLLLOO TWINFLOW and Owen-Image-LLgee (1-NFE). The prompts and generated images sourced from DPG-Bench. We observe that Qwen- owen-Image-Lightnnn tend generate very similar images though noise different, which hurts diversity. model remains diversity and high quality generation.
a photo of and zebra
photo of a cow below an airplane
200 steps
400
800 steps
1600 steps
3200 steps
6400 steps
Figure 6: Visualizations of 1-NFE images generated Qwen-Image- Meeaage-Tiig TWINFLOW w.r.t. training steps. In the early stages of training, our method converges rapidly, and generated images begin take shape (200 400 steps): as training progresses, our method gradually optimize the visual details (800 to 6400 steps).
Subsequently (800 6400 steps), the training process shifts towards refining finer visual details, leading to a steady enhancement of image fidelity. This highlights our approach's 's efficiency in achieving strong initial results and its capacity for continued improvement with extended training.
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E.3 SELECTED PROMPTS USED FOR VISUALIZATION
The prompts used to generate the results shown in Fig. and Fig. are detailed in this section to ensure reproducibility.
cinematic vertical composition of bustling street in Central Hong Kong. featuring vibrant red taxis driving along the road. The scene is framed by towering modern skyscrapers with reflective glass facades, creating urban jungle atmosphere. The cityscape glows with neon signs and soft ambient light, capturing the essence of Hong Kong's iconic night-time energy. On the road surface, painted traffic markings texts: 'SLOW". The sky above has a gradient transitioning from deep twilight blue to warm orange hues near the horizon, adding depth and drama to the image. Rendered in heper-realistic style with rich colors, intricate textures. and high contrast lighting for maximum impact.
Classic styoque-style still life painting. woven wicker basket overflowing with fresh fruits including red and green grapes, ripe apples, plums, quinces, and yellow pear, adorned with grape leaves and vines. The basket sits on a draped stone or wooden table covered with dark blue cloth, with scattered fruits and berries around it. Rich, dramatic lighting highlights the textures and colors of the fruit, creating deep shadows and soft highlights. A luxurious red curtain drapes in the background, adding depth and contrast. Realistic, highly detailed, oil painting style, reminiscent of 17th-century Dutch Flemish masters such as Jan Davidsz de Heem or Caravaggio. Warm, earthy tones, meticulous attention to detail, and a sense of abundance and natural beauty. Ultra HD, 4K, cinematic composition.
Clean white brick wall, vibrant colorful spray-paint graffiti covering entire surface: top giant bubble letters 'I
Step Generation', below stacked 'TwinFlow', 'Made Easy' in rainbow palette, fresh wet paint drips, daylight urban photography, realistic light shadow. Ultra HD, 4K, cinematic composition. close-up realistic selfie three cats of different breeds in front the iconic Big Ben, each with a different expression, taken during the blue hour with cinematic lighting. The animals are close to the camera, heads touching, mimicking selfic pose, displaying joyful, surprised, and calm expressions. The background showcases the complete architectural details of the [landmark]. with soft light and warm atmosphere. Shot in a photorealistic, cartoon style with high detail.
Starbucks miniature diorama shop. The roof is made of oversized coffee beans, and above the windows a huge 'Starbucks' sign. A vendor is handing coffee to customers, and the ground is covered with many coffee beans. Handmade polymer clay sculpture, studio macro photograph, soft lighting, shallow depth of field. Ultra HD, 4K, cinematic composition.
whimsical scene featuring a capybara joyfully riding sleek, modern rocket. The capybara is holding a sign with both hands, the text on the sign boldly and eye-catchingly reading
The capyhara looks thrilled, sporting playful grin as it soars through a vibrant sky filled with soft, pastel clouds and twinkling stars. The rocket leaves trail of sparkling. colorful smoke behind it, adding to the magical atmosphere. Ultra HD, 4K, cinematic composition.A still frame from black and white movie, featuring a man in classic attire, dramatic high contrast lighting, deep shadows, retro film grain, and nostalgic cinematic mood. Ultra HD, 4K, cinematic composition.
Close-up portrait of young woman with light skin and long brown hair, looking directly the camera. Her face illuminated by dramatic, slatted sunlight casting shadows across her features, creating a pattern of light and shadow. Her eyes are a striking green, and her lips are slightly parted, with a natural pink hue. The background is soft, dark gradient, enhancing the focus on her face. The lighting is warm and golden. Ultra HD, 4K, cinematic composition.
field of vibrant red poppies with green stems under blue sky.
small blue-gray butterfly with black stripes rests on a white and yellow flower against blurred green background.
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A grey tabby cat with yellow eyes rests on a weathered wooden log under bright sunlight
E,4 HIGH RESOLUTION VISUALIZATION
In this section, we showcase further qualitative results from Qwen- Iieen-Image-ie Iwwge-mmaanaLLLLOO TWINFLOW to highlight its generative capabilities. To ensure an unbiased representation, the generation prompts were chosen at random, and the resulting visualizations are presented without curation or cherry-picking
E.5 FAKE TRAJECTORY VISUALIZATION
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Published as a conference paper at ICLR 2026
Pulp
Figure 7: Visualization of Qwen-Image-TWiNEE TWINFLOW (NFE=4). Each image of 1328 x 1328 resolution.
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Published as a conference paper at ICLR 2026
TOHRN
TyiNID
GLSAD
Figure 8: Visualization of Beee--Imagg-TwiNNF.ccoo (NFE=4). Each image is of 1328 x 1328 resolution.
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Published as a conference paper at ICLR 2026
Figure 9: Visualization of Qwen-Image- TWINFLLOW (NFE=4). Each image is of 1328 x 1328 resolution.
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Published as a conference paper at ICLR 2026
Figure 10: Visualization of Twen-Image-TwiNFF.ov (NFE=4). Each image is of 1328 1328 resolution.
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Published as a conference paper at ICLR 2026
Figure 11: Visualization of Owen--magg-TTWNNFL..x (NFE=4). Each image is of x328 1328 resolution.
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Published as a conference paper at ICLR 2026
Figure 12: Visualization of the fake trajectory (NFE=20), The fake images have significant visual difference comparing real images.
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Reviews:
[{"review_id": "PWu6qJclKo", "reviewer": "Reviewer_xzEE", "summary": "This paper introduces TWINFLOW, a novel framework for training one-step generative models that achieves efficient inference without requiring auxiliary discriminators or frozen teacher models. The key innovation is the \"twin trajectory\" concept, which extends the time interval from [0,1] to [-1,1], creating paired trajectories for real and fake data generation. By minimizing the velocity field differences between these trajectories, the method achieves self-adversarial training. The authors demonstrate TWINFLOW's effectiveness by successfully applying it to Qwen-Image-20B, achieving competitive 1-NFE performance that matches 100-NFE baselines while reducing computational costs by 100×.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Successfully applying the method to 20B parameter models demonstrates promising scalability.\n- The 1-NFE performance closely matching 100-NFE baselines is impressive.", "weaknesses": "- Theoretical flaws. If I understand correctly, the paper trains a model with a single-timestep condition to handle generation at arbitrary steps. **However, such a model is no longer a score model, and using it in reverse KL to compute the gradient of logp is theoretically flawed**.\n- BLIP-3o-60K contains samples carefully generated using GenEval prompts, and training on BLIP-3o could yield very high GenEval scores. GenEval score is the primary metric used in this paper and highlighted in the abstract, which leads to an unfair comparison in this paper.\n-  I think the benefits of integrating the real score, fake score, and generator—three models together—have been overly claimed. Particularly in the Qwen 20B example cited by the authors, all models use LoRA. For the distillation by reverse KL method, integrating two LoRAs onto the same model backbone does not bring about notable memory costs. And the GAN loss is an additional benefit; we can directly remove it and still achieve high performance. \n- The combination of reverse KL and consistency-like RCGM is straightforward. Besides, similar combinations have been explored in sCM [a].\n- The \"twin trajectory\" has been explored in FACM [b]. However, no discussion is found in the paper.\n\n[a] Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.\n[b] Flow-Anchored Consistency Models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "girfYCR4W4", "reviewer": "Reviewer_ESMU", "summary": "TwinSteps is a framework that aims to reduce the number of inference steps in diffusion / flow matching models to as few as 1 step. It bypasses the need for distillation and avoids standard adversarial training (e.g. GAN style). The approach strongly builds on the RCGM method, but adds three main additional components: extending the time domain from [0,1] to [-1,1] and adding an adversarial as well as a rectification loss. On text-to-image tasks, the method outperforms multiple baselines and is shown to be scalable by applying it on the largest open-source multi-modal model, Qwen-Image-20B.", "questions": "- In Eq. 1 it is not defined what $f_\\Theta-$ is?\n- In Eq. 2, does $z^{fake}$ correspond to $z$ that is also used in the base component of the loss to ensure alignment?\n- Eq. 2 has the objective to learn the negative time time trajectory. Why is it chosen differently to the normal base loss?\n- In Eq. 8 and 9: Did I understand correctly that $x_t$ corresponds to $x_{t’}^{fake}$?\n- On intuition and explanation of weaknesses: Can you give more information on the addressed point?\n- Instead of using a negative time trajectory, would a simple boolean condition as model input that indicates fake or real data also work? \n- Given the adversarial components do help the 1 / 2 step predictions, do they also help to improve many step predictions e.g. 50 or 100 NFE’s ?\n- On the Qwen-Image-Lightning diversity discussion: The shown results in the appendix support the “almost identical image” claim, but do - not seem sufficient. Can you quantify or provide more examples to support this claim?\n- In Table 2: Why is the performance increase of the method better on Qwen-Image than on OpenUni for 1 and 2 steps?\n- Can you clarify if TwinFlow-0.6B and TwinFlow-1.6B leverage a pretrained backbone that is full parameter finetuned or if these two models are trained ab-initio / from scratch? What about the ImageNet-1K results mentioned in the appendix?\n\n- Minor comment:\nTypo in Line 201: “to match with us”, probably “to match each other”?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- While many distillation or few step approaches leverage additional trained or teacher networks, this method does not require any additional separate network.\n- While strongly leveraging ideas by the DMD (Distribution Matching Distillation paper) paper, the authors found an elegant way to implement the idea of an adversarial network component into the existing flow matching / diffusion frameworks by extending the time domain to [-1,1] where [-1,0] corresponds to the fake data domain.\n- The presented method shows strong results on the image generation benchmarks GenEval, DPG-Bench and WISE", "weaknesses": "- Intuition and explanation:\nWhile the intuition of an adversarial component is clear in DMD is clear, certain design choices had to be taken to make this method work: \na) Representing fake data by negative time steps\nb) Having the adversarial loss learning the negative trajectory\nc) A rectification loss that should straighten the trajectory\nFurther, during training N=2 is adopted, meaning 2nd order approximations are done in the RCGM framework, giving a strong few-step prior.\nThe paper does not fully explain why the negative time trajectory is required and why the rectification component ultimately converges to a single step solution. If this was not investigated but only empirically observed, this needs to be clearly pointed out\nThe strength of the method could be better explained in more simple settings such as CIFAR10 or Image1K.\n\n- Evaluation and Results:\nAlmost all evaluations have been done on text-to-image benchmarks. This comes with additional complexity in the evaluation as e.g. diversity of images for the same text prompt multiple times (as pointed out by the authors in Table 2). While not explicitly mentioned in the main paper, the method seems to perform worse on other (simpler) benchmarks as e.g. Image-1K 256x256. In fact the 2 step FID of Twinflow trained for 801 epochs mentioned in the appendix (FID=2.05) is larger than the pure RCGM FID value after 424 epochs (FID=1.92, see https://github.com/LINs-lab/RCGM/blob/main/assets/paper.pdf). \nQwen-Image-Lightning is 1 step leader on the DPG benchmark and should be marked like this in Table 2\n\n- Distillation / Fine Tuning vs. Full training method:\nQwen-Image-TwinFlow (and possibly also TwinFlow-0.6B and TwinFlow-1.6B, see question below) leverages a pretrained model that is fine-tuned. In this case the pretrained model somehow acts as a “teacher” adding stability to the fine-tuning i.e. as the fake data predictions. \nHowever, when training from scratch the optimization problem becomes significantly harder. Possible stability issues and their solutions are not discussed in the paper.\n\n- Unclear writing:\nIn several parts of the paper, i.e. the methodology section, it is not always easy to follow the equations as variable names are not always defined or used in a consistent manner (see questions below)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HsmiCC3xPj", "reviewer": "Reviewer_qqUP", "summary": "This paper proposes a new one-step generation method named TwinFlow. The method applies a self-adaversarial manner: First, it extend the common $[0,1]$ time interval into $[-1,1]$. Then it uses the $[0,1]$ and $[-1,0]$ interval for training a true and fake diffusion model, respectively. And a loss is used to minimize the difference of the true and fake velocities. Experiments show that the proposed method achieves strong performance on text-to-image tasks, and also verify its scalability on a 20B model.", "questions": "- How do you generate $\\\\boldsymbol{x}^{\\text{fake}}$? I suppose $\\\\boldsymbol{x}^{\\text{fake}}=\\\\boldsymbol{x}\\_t+(0-t)\\\\boldsymbol{F}_{\\theta}(\\\\boldsymbol{x}\\_t,t,0)$ under the OT-FM framework. If so, is the sample generated by the diffusion ODE idential to that generated by the generator trained wiith DMD loss (rectify loss in the paper) in particle or just in distribution? How do this loss boost the 2 or more-step performance?\n- In DMD and VSD, the training is alternatively (fake score net and generator). Why can your method be free from alternate training?\n- I found that the authors did not discuss the usage of CFG in the method, and I also notice that in Figure 3, the TwinFlow results are without CFG. I wonder if CFG is applicable in TwinFlow, or can CFG improve the performance further if applicable?\n- Is the DiT model on ImageNet trained from scratch or with teacher weights loaded? And, what is the 1-step performance? Also, are the results with or without CFG? If the $2.05$ FID is arrived without CFG, that is impressive.\n- While there are multiple scores reported for 1-step generation, there are very few 1-step visualizations. Can you provide some (it won't affect my evaluation)?\n- In consistency training/finetuning, the additional training cost compared to diffusion models is one forward pass. In your method, the extra cost seems much more. Can you elabarate the extra cost, and compare the practical memory and speed with consistency training (e.g., on ImageNet)?\n- The $\\lambda$ parameter only adjust the effect of $\\mathcal{L}_\\text{TwinFlow}$; do you also need to adjust the effect between adv loss and rectify loss?\n- Is there a missing \"-\" in Eq. (6)?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- I like its feature where only one model is trained, and that it achieves good performance on T2I models without GAN. Compared to SiD and VSD, it combines the pretrained teacher and the true score net, while makes the fake score net online, which is a good improvement.\n- The performance is strong, especially for the scalabity on large-scale (20B) models.", "weaknesses": "- This method is similar to existing distillation methods like SiD, DMD and VSD, for example, DMD requires a trained diffusion teacher and a online-training generator, corresponding to the $t\\in [0,1]$ part of TwinFlow; the auxiliary fake score network corresponds to the $t\\in[-1,0]$ part. The authors should provide a detailed comparison in their paper.\n- Since this approach integrate the function of the three models in DMD into one model, there is potential performance degration since the total model capacity is limited.\n- Though with better performance, the forward and backward pass of TwinFlow seems much more complicated than consistency training, the authors are expected to provide the theoretical and practical training cost comparison (also see questions).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)