FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows
Authors: Yeonwoo Cha
Semin Kim
Jinhyeon Kwon
Seunghoon Hong
Venue: ICLR 2026 Poster (2026)
Keywords: Generative models
Flow matching
any-to-any generation
Primary area: generative models
TL;DR: We propose an efficient flow-based multimodal any-to-any generation model with bidirectional flows.

Abstract: Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. 
Despite recent success, existing flow-based approaches are challenged by their inefficiency, as they require large-scale datasets often with restrictive pairing constraints, incur high computational cost from modeling joint distribution, and rely on complex multi-stage training.
We propose FlowBind, an efficient framework for any-to-any generation. 
Our approach is distinguished by its simplicity: it learns a shared latent space capturing cross-modal information, with modality-specific invertible flows bridging this latent to each modality. 
Both components are optimized jointly under a single flow-matching objective, and at inference the invertible flows act as encoders and decoders for direct translation across modalities.
By factorizing interactions through the shared latent, FlowBind naturally leverages arbitrary subsets of modalities for training, and achieves competitive generation quality while substantially reducing data requirements and computational cost.
Experiments on text, image, and audio demonstrate that FlowBind attains comparable quality while requiring up to 6× fewer parameters and training 10× faster than prior methods. The project page with code is available at https://yeonwoo378.github.io/official_flowbind.

Published as a conference paper at ICLR 2026
FLOWBIND: EFFICIENT ANY-TO-ANY GENERATION WITH BIDIRECTIONAL FLOWS
Yeonwoo Cha'
Semin Kim
Jinhyeon Kwon
Seunghoon Hong
KAIST
{ckdusdn03, semink i jh. kwon, seunghoon. hrrighhoon.hnng 0kaiss ac..ac..e
Equal contribution
ABSTRACT
Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, existing flow- based approaches challenged by their inefficiency, they require large-scale datasets often with restrictive pairing constraints, incur high computational cost from modeling joint distribution, and rely complex multi-stage training. We propose FlowBind an efficient framework for any-to-any generation. Our ap- proach is distinguished by its simplicity: it learns shared latent space capturing cross-modal information, with modality-specific invertible flows bridging this la- to each modality. Both components are optimized jointly under single flow- matching objective, and at inference the invertible flows act as encoders and de- coders for direct translation across modalities. By factorizing interactions through the shared latent, FlowBind naturally leverages arbitrary subsets of modalities for training, and achieves competitive generation quality while substantially reducing data requirements and computational cost. Experiments on text, image, and audio demonstrate that FlowBind attains comparable quality while requiring up to 6x fewer parameters and training 10x faster than prior methods. The project page with code is available at phtttt::: httt::::/yyewwoo333 github. lo/official fl fowbi nd
INTRODUCTION
Recent progress in flow-based generative models has delivered state-of-the-art performance in multi- modal generation. By conditioning on a given input modality, these models excel at specialist tasks such text-to-image (Esser et al. 2024 Black Forest Labs. 2024) text-to-audio synthesis (Liu et al. 2024 Huang et al. 2023), demonstrating their strength in learning continuous cross-modal transformations However, these successes largely confined fixed input and output mapping, and extending flow models to support true any-to-any generation, where arbitrary subsets of modal- ities can be generated given any other subsets, remains an open challenge.
Bridging the gap from specialist to generalist flow models introduces fundamental hurdles, primarily due to requirements of multi-modal data and computational cost. Frameworks that rely on a central anchor modality, typically text (Tang et al. 2023), require each modality to be paired with text during training that all modalities can be aligned through the shared text representation. This design is restrictive, as it prevents the model from learning the rich, direct correlations that exist beyond language. Conversely, methods that model the full joint conditioning of all modalities (Li et al. 2025b) can achieve expressive generation performance but at a steep cost: they require some fully-paired data for stable training, which is scarce, and their computational complexity often scales quadratically with the number of modalities. These data and compute issues render them impractical for real-world scenarios with a large and diverse set of modalities.
Beyond the computational cost, significant hurdle for generalist models is the complexity of their training pipelines. Rather than a single, unified process, these frameworks often rely on intricate, multi-stage procedures. These stages separately optimize the encoding components for modality alignment and the decoding components responsible for the model's generative capabilities. This staged approach is evident in prominent models; for instance, CoDi (Tang al. 2023) employs

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multi-stage process that separates modality alignment from joint generation. Similarly, OmniFlow (Li et al. 2025b) requires a distinct post-training phase after merging its core components. Such multi-stage pipelines be brittle, difficult to optimize, and hinder the development of truly seam- less, end-to-end generative models.
We introduce FlowBind, simple flow-based model that addresses these limitations. FlowBind introduces a learnable shared latent capturing cross-modal commonality, and connects each modality this latent through its own invertible flow. All components trained jointly under single flow matching objective, while the learned flows enable direct any-to-any translation at inference. Because each flow requires only its modality paired with the latent, the method naturally supports training with partially paired data while reducing computational cost. This design yields simple, efficient, and data- feta-flexible solution for general-purposs any-to-any generation.
In summary, our main contributions are as follows: (1) We introduce a flow-based framework for any-to-any generation that factorizes multi-modal interactions through a learnable shared latent, enabling training from arbitrary paired data with low computation budget. (2) Our method jointly optimizes both the shared latent and all modality-specific flows under a single flow-matching loss, avoiding the multi-stage pipelines. (3) Experiments text, image, and audio demonstrate that FlowBind achieves competitive quality with substantially reduced data and computation compared to representative baselines, while flexibly supporting any-to-an any translation.
2 PRELIMINARIES
Flow Matching Conditional Flow Matching (Lipman et al. 2223) is simulation-free framework for learning a continuous transformation between source distribution po and a target distribution da, Pr. This transformation is defined by an Ordinary Differential Equation (ODE), at = vg (z (zr, f), where a drift network ve parametrizes the time-dependent vector field. With linear interpolation
path 24 = (1 (1-th t) 2 + +ta 21 with (Zo, 21) ~ P1), the target velocity is simply 21 20, and the objective becomes:
CrM (0) = E [1100 t) - (21 - 20)ll
(1)
At the optimum, Eq. yields the conditional expectation of the target velocity:
(x, t) B| žτ
Generation then performed by integrating the learned drifts over time:
Ato + 1// og (2x,,
ODESoLve(2.. Uo, lo L1)
(3)
Note that, under standard Lipschitz conditions, the induced flow invertible i.e., the ODE can be integrated forward backward in time to induce samples from Po p1.
Any-to-Any Generative Flows The goal of any-to-any generation is to learn unified model that can translate between arbitrary subsets of modalities. Given N modalities z (2 ...., N. this amounts to modeling their joint distribution p(z) so that for any Sm. Saut C [I....,Ny the model can perform any-to-any generation by sampling from conditional probability Sm Sm)).
Existing flow-based approaches address this problem by constructing continuous trajectories that transform i.i.d. Gaussian noise Zg Tprior into data samples Z Tdata- Representative examples include CoDi (Tang et al., 2023) and OmniFlow (L.i et al. 2025b), which mainly differ in how they synchronize trajectories across modalities. CoDi learns modality-speciie
encoders that align all modalities to shared text embedding, which then serves the conditioning signal for per- moddalyyy denoising networks e' (zf, t, dlex)). In contrast, OmniFlow learns a time-decoupled joint velocity field 0(2), ... = f1,.... ... 1.), where the interpolation path for each modality is explicitly conditioned with the other modalities to ensure alignment.
Despite their empirical success, existing flow-based methods face several limitations. First, they cannot fully leverage arbitrary paired modalities for any-to-any generation: CoDi requires each modality to be paired with text establish canonical embedding, while OmniFlow relies heavily
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Training
Inference
Shared Latent
Forward
Example training
Backward
Text- lmag-lmaae
Image
Trrrngh
Text
Text
Image
"A baby cat meowing on the gross"
"A baby cat meowing on the grass"
Audio
Audio
(a) Training both shared latent and drifts.
(b) Inference with per-modality drifts.
Figure I: An overview of FlowBind. (a) During training, we jointly learn the shared latent and per-modality drift networks single stage. (b) At inference, the learned drift networks perform flexible any-to-any generation by solving per-modality ODEs forward and backward in time.
on fully-paired data (i.e., samples where all N modalities are present) for stable training Sec- ond, both methods require multi-stage training: CoDi separately learns the shared representation and denoising networks, whereas OmniFlow pre-trains drift networks for each modality pair be- fore joint training. Finally, they operate in high-dimensional representations, leading to substantial computational cost and slow convergence.
FLOWBIND
To address the aforementioned challenges, we propose FlowBind, a unified flow-based framework for any-to-any generation. FlowBind is designed overcome key drawbacks of prior methods: supports single unified training procedure, operates with lower computational overhead, and fully exploits partially paired data for effective learning.
The key idea of FlowBind is to replace the fixed Gaussian prior with a learnable, shared distribution that encapsulates common information across modalities. This acts latent anchor, where each modality is connected to it via their own invertible, per-modality flows (Figure TT. With this factor- ization, FlowBind achieves alignment across modalities naturally via the shared distribution, unlike existing approaches that anchor all modalities to text (Tang et al.) (203) or couples them through joint drift (Li et al.) 2025b). Meanwhile, both the shared distribution and the per-modality flows are learned jointly with only standard flow matching loss using partially paired data.
Formally, consider a subset of multi-modal data S {a*li E S} with S C {L...., sampled from a joint distribution d ~ Th Assume that there exists shared latent 2" T that encompasses the common information of all individual modality in z Then for each E S. Flow Bind learns a straight interpolation path that bridges the data z shared latent by:
NY. which is
2 = tz + (1-t)²²
(4)
d= = o ('(2) 1), a1
(5)
where of denotes the -ppecit velocity field. Note that multi-mode flows are factorized per modality given the shared latent (Eq. 4 and the shared latent implicitly aligns these flows across modalities (Eq. 5). During training, the shared latent instantiated as =" Hz(2S) through an auxiliary encoder Hg. whose marginal approximates Tatared and is optimized jointly with the per- modality drift networks Vg. (Figure [a)). At inference, FlowBind relies only on the learned drift networks: owing the invertibility of the direct flows, both inferring the shared latent from input modalities and generating outputs from the latent are achieved by a single drift network per modality (Figure (b)). Details of the training and inference procedures are provided in Section 3.1
Atthough partially paired data can be used for training in principle, performance and stability are reported depend strongly fully paired data; Appendix B.2 Li et 20256).
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Following prior works (Esser et al. 2024 Liu et al. 2024). FlowBind operates in a compressed latent space obtained by per-modality autoencoders. However, instead of low-level, high-dimensional la- tent, we adopt compact and semantic representations extracted by strong encoders in each modality, paired with decoders that reconstruct modality-speciffi details from the encoded feature. This design enables FlowBind to focus shared structure in low-dimensional space, making cross-modaae mrosstmodality alignment simpler and training both faster and efficient.
Taken together, FlowBind provides several advantages over existing approaches. By introducing shared latent space, FlowBind factorizes the multi-modal flow into independent per-modality drifts, allowing them to operate isolation with reduced computational cost. This factorization also natu- rally enables training with arbitrary paired modalities: since each drift network learns only to con- nect its modality to the shared latent, learning does not depend on specific modalities or fully-paired data. Finally, both the shared latent and modality-speciff drifts are optimized jointly with a single flow matching objective, avoiding the multi-stage training pipelines of prior works and yielding a simple and efficient framework.
3.1 TRAINING AND INFERENCE
Learning Objective During training, the auxiliary encoder H& and the set of modality-specific drift networks {uge ) are optimized jointly under the flow matching framework in Eq. and 5
Given a partially paired sample z², the auxiliary encoder produces a shared latent H (z")
and for each modality i E S. the drift network rg.. is trained to approximate the velocity field along the path between 2' and z². This leads to the training objective:
Σ (1e (2). (2' LiE
-( (e'-=))]()
[ の) = 2
(6)
where 0 = (0',...,00 ) In principle, this couples the two components: drift networks learn to predict the displacement toward each modality endpoint, while the auxiliary encoder is encouraged to provide a shared latent from which every modality can be recovered to aid drift networks.
However, this formulation admits degenerate solutions. For example, if the encoder collapses to constant output such as =² 0, the drift networks can trivially fit v'(zt, () = 2² with =f = L2 and achieve the zero loss for t E (0, 1], leaving the encoder with meaningful supervision (Kim iet al. 2024). The underlying reason is that flow matching enforces transportation between two fixed endpoints but does not itself constrain the distribution of encoder outputs. Prior works on direct flow (Liu et al., 2025 He et al., 2025) address this by adding explicit regularizers, such as contrastive losses the encoder, but these introduce additional computation, hyperparameters, and scalability bottlenecks especially with increasing number of modalities.
In contrast, show that both stabilization and meaningful learning of the encoder can be achieved within the flow-matching objective itself. Our approach is simple: for E (0, 1], we stop gradients through the auxiliary encoder stably train the drift networks, while at t=0, the encoder directly updated together with the drifts. Despite its simplicity, this scheme effectively prevents collapse and provides the encoder with a meaningful learning signal, as we elaborate below.
Analysis on Encoder Objective To understand what the auxiliary encoder learns under our train- ing strategy, we analyze the flow matching loss at t=0. Substituting the Bayes-optimal drift v* (Eq. 2 into Eq. 6 at = 0 gives encoder's effective objective:
(2,, t)
((
T [|] v" (=",0) (=' - ==" [ = E [I] R|= |=*] - 2' [| = B[Var(=') |=*)]-
(7)
This shows that, at /=0, the encoder is explicitly optimized to minimize the conditional variance of each modality given the shared latent. The term [ [Var(=* ==)]] often referred to as the unexplained variance, measures how much information about modality i remains outside of 2*. By the law of total variance (Grimmett & Stirzaker, 2001). reducing this quantity equivalently increases the explained variance of z' by z² Since the optimization is carried out jointly across all modalities, the encoder therefore driven to shape 2 so that it retains predictive information about each modality, ensuring that the shared latent becomes increasingly informative for cross-modal alignment.
More generally, when the drift networks are not optimal, Eq. 6 t=0 decomposes into unexplained variance and an approximation error of the drifts:
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Proposition 1 (Equivalence at t = 0) For any parameters (8, Φ) and modality subset S, the flow matching loss (Eq. 6) at = decomposes as follows:
[ Φ) E Var(z* |²"] +
E
Uo (z", 0) E - [1]
ES
EES
unexplained variance
oppreximation ervor of
A formal proof is provided in Appendix
This decomposition reveals that even when the drift networks are imperfect, the auxiliary encoder H& is consistently driven to minimize unexplained conditional variance while simultancously opti- mizing the shared latent to enable better drift approximation of their targets. In this way, our training strategy encourages the auxiliary encoder and the drift networks to remain tightly coupled: the drifts learn to predict each modality endpoint from the shared latent (t E [o, ID. while the encoder is driven shape the latent into representation from which all modalities can be reliably recovered
(t = 0). During training, we balance the drifts and encoder training by sampling from the mixture t Unif(( 1) + @ 0). The training procedure is given at Algorithm !
Inference After training. FlodBind performs versatile any-to-any generation relying solely on the learned per-modality flows, without utilizing the auxiliary encoder. Given a source modality i, we first project it onto the shared latent by integrating its backward flow, and then map the shared latent to the target modality j via the corresponding forward flow:
ODESolve(z', rg., 1),,
= ODESolve(E", 0,
(8)
When conditioning on multiple source modalities z FlowBind obtains per-modality latent esti- mates a(+.5) by solving the corresponding backward flows independently. These estimates are then aggregated into the shared latent 8* by simple averaging. Finally, the target modality is generated by integrating its forward flow starting from 8r The inference procedure is given at Algorithm
4 RELATED WORK
Any-to-Any Generation A prominent paradigm for any-to-any generation tokenizes all modal- ities into a discrete space and trains a single sequence model to predict the unified stream auto- regressively. In this setup, a powerful large language model performs gross-modal sequence gener- ation, with tokenized data of modalities. Some works (Team] 2024 Kou et al. (2255 focus on interleaved generation solely on text-image generation, while others (Wu et al. 2024 Zhan et al.) 2024) extend to broader multi-modal scenarios including speech (Wang et al. 2024b) and even robotics (Lu et al.) 2024). Training such models typically involves multi-stage procedures and often relies on instruction fine-tuning, which requires datasets with detailed textual descriptions and usu- ally depends on large language models. Consequently, text-paired data is often required for training.
Another line of work utilizes discrete diffusion models, often by adapting them to operate on dis- crete token spaces (Rojas et al., 2025 Shi et al., (025). These methods, which typically focus on text-image generation tasks, leverage the high-quality synthesis capabilities of diffusion for multi- modal scenarios. For instance, UniDise (Swerdlow et al. (2255) highlights the controllability of this approach by framing various conditional generation tasks, such as inpainting.
Direct Flow-based Models Recent flow-based models have explored learning direct, data-to-data invertible mappings between two data distributions (Li al. 2023; Wang al., 2024a). predom- inantly focusing on text-image pairs (Liu et al., 2025 He et al.) (2025). This approach represents fundamental departure from traditional generative flows that typically learn bridging from fixed prior distributions (e.g., standard Gaussian) to target data distributions through conditional genera- tion mechanisms. To facilitate these direct transformations, existing methodologies designate latent learnable embedding. This is achieved
distributions of one modality (i.e., source distribution) by introducing an encoder for the source modality and constructing additional loss terms that align the source and target modalities, such as contrastive learning objectives.
While our approach shares foundational ideas with prior work, its emphasis and formulation differ. Existing methods typically rely on multiple loss terms to stabilize training and to optimize endpoint embeddings; in contrast, we employ single, unified flow objective to achieve the same optimiza- tion. Moreover, we pursue direct flows for multi-modality connectivity, whereas most prior efforts have concentrated on two-modality settings, especially text- eext-image generation.
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Comparison of computational cost. #(A-B) indicates the number of training samples for each dataset combination. Training time for CoDi is omitted due to absence of training code and details. For OmniFlow, we report the training time only for the final joint training stage.
Table 1:
Number of Traning Data
Model
Train Param.
GPU-hr
Joint Training
#(T-I)
#(-A))
#(-A)
#(T-A-1)
CoDi
4.3B
400M
3.5M
1.9M
NO
OmniFlow
3.2B
480hr*
28M
2.4M
2.2M
NO
FlowBind
568M
48hr
310K
96K
180K
YES
5 EXPERIMENTS
We conduct an extensive evaluations on any-to-any generation tasks across text, image, and audio modalities, covering a wide range of input-ouput modality combinations including one-to-one, one- to-many and many-to-one generation tasks. For baselines, we mainly consider approaches on flow- based any-to-any generative modeling, namely CoDi (Tang et al., 2023) and OmniFlow (Li et al.) 2025b). Qualitative results for various input-output modality combinations official project page: /yop::://yyoooo0333 8 github io /oo.ffifiaaa.aaooooooooo. flowbind
available
Tasks and Evaluation Protocol For one-to-one generation, we consider all six possible tasks that consist of text, image and audio, and discuss its result in Sec. 5.2 We adopt established evaluation metrics for one-to-one generation, where standard measures of generation quality and cross-modal alignment are available. Specifically, generation quality assessed using established modality- specific measures: FID (Heusel et al. (017) for images, FAD (Kilgour et al.) (019) for audio, and CIDEr (Vedantam et al. 2015) for text captions. Cross-modal alignment is evaluated through pairwise similarity metrics: CLIP scores for text-image pairs (Hessel et al.) (021). CLAP scores for text-audio pairs (Elizalde et al. 2023). and Paadio-Imaee-Sinnn Similarity (AIS) (Wu et al. (022) for image-audio pairs. Evaluations
done at held-out test set for text-audio and image-audh tasks, while we employ widely adopted zero-shot benchmark in MS-COCO for eoxt-to-imaee and image- to-text tasks. Detailed evaluation protocols and descriptions of the specialist baseline models are in Appendix[]
For many-to-mane -t--ymyy generation, conduct qualitative analysis of more challenging settings to validate FlowBind's cross-modal capabilities. Furthermore, since no standard benchmark exists for many-to-many generation, we introduce tailored quantitative evaluation metric and report corre- sponding results in Sec. 5.3 demonstrating that FlowBind consistently outperforms the baselines.
Implementation Details We employ EmbeddingGemmas (Taam al. 2025) for textual semantic latent, CLIP (Radford et al. (02T) for visual latent with Stable-UnCLiP (HuggingFace) 2025) as decoder, and CLAP (Elizalde et 2023) features for audio synthesis conditioning. Note that these modality-specine encoders and decoders are frozen during the training of FlowBind. We employ MLP-based architecture with residual connections for both auxiliary encoders and drift networks, with AdaL for time modulation (Peebles & Xie 2023). More detailed information, including the architectural and training specifications, can be found in Appendix C
5.1 INSTANTIATION OF FLOWBIND
To highlight FlowBind's training efficiency, we instantiate FlowBind as relatively lightweight model and train it on smaller dataset, jointly learning modality-specific drift networks and shared latent space. We summarize the details of our instantiation of FlowBind in Table I making com- parison to previous flow-based any-to-any generation models. Compared to baselines, Flow achieves any-to-any generation with considerably less computations and efforts. When compar- ing the computational cost, FlowBind operates on low-dimensional, compact representation space, yielding a lightweight model with less than IB trainable parameters. This design choice makes Flow Bind to be trained much faster, using about 10x less compute compared to OmniFlow, in terms of GPU-hours. We also use much smaller data compared to the baselines (0.15 % of CoDi or 1.79 % of OmniFlow). Overall, these comparisons position FlowBind as practical and scal- able framework for any-to-any generation with substantially reduced compute and data budgets. In subsequent sections, we demonstrate that our efficient any-to-any generation model attains strong cross-modal generation performance.

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Table 2: Fidelity assessment on one-to-one evaluation benchmarks.
T-1 1
I-T
T-A
A
I A
A
Category
Model
FID J CIDEr
FAD
CIDErt
FAD I FID
SD3-Mcdium
25,40
FLUX.11
22.06
LaVAA-NeXT
109.3
Specialists
TangoFlux
1.41
AudioX
3.09
Qwen2-Audio
4.64
Seeing & Hearing
5.31
Sound2Vision
42.55
UnifiedlO2-L
21.54
134.7
8.31
12.15
CoDi
24.80
16.40
9.84
6.62
14.58
50.4
Generalists
OmniFlow
22.97
44.20
4.20
31.79
5.67
106.03
FlowBind
17.39
46.26
4.19
55.11
2.50
26.60
Table 3: Alignment results on one-to-one evaluation benchmarks.
T T
I-T
T
A CLAP
I I A AIS
Category
Model
CLIP
CLIP
CLAP
AIS f
SD3-Medium
31.60
FLUX.1
31,06
LLaVA-NeXT
32.14
Specialists
TangoFlux
42.71
AudioX
29.29
Qwea2-Audio
17.09
Seeing & Hearing
75.11
Sound2Vision
62.39
Unified1O2-L.
30.71
30.73
13.48
18.68
CoDi
30.26
26.24
10.79
17.94
61.55
74.26
Generalists
OmniFlow
31.52
27.71
24.23
45.08
71.71
59.22
Flow Bind
28.35
29.74
29.08
36.70
82.89
78.17
5.2 RESULTS ON ONE-TO ONNE GENERATION
Effectiveness of FlowBind We demonstrate the effectiveness of FlowBind under all six pairwise one-to-one generation scenarios in Table / and Table 3 While the core goal of FlowBind lies on efficient modeling of any-to-any generation, we also observe the resulting model shows strong capability in cross-modal generation tasks. Compared to CoDi and OmniFlow, FlowBind achieves the best quality metrics in all six one-to-one generation tasks, while showing superior alignment score on four tasks among six. We also note that baselines such as OmniFlow are initialized from strong specialist model (i.e., SD3-Medium) that excels at text-image alignment, which explains their particularly good performance on text-to-image alignment scores. We compare Flow Bind with UnifiedIO2-L (Lu et al. 2024), recent LLM-based any-to-any generation method. FlowBind is comparable on text-image tasks and stronger in other settings, suggesting that our formulation may provide benefits beyond flow-based methods. As an overall, conclude that FlowBind shows promising performance on the evaluated one-to-one generation tasks.
An interesting observation is that FlowBind exhibits substantial gains in the image-audio genera- tion. where it significantly outperforms among generalists and even dedicated specialist, without making poddality-speciffe adjustments. We conjecture the impressive performance of FlowBind at audio-image correspondence stems from the introduction of learnable shared latent space, which is designed to contain meaningful information about each modality (Section 3.1) and learns directly from audio-image pair. Instead of learning shared latent space from arbitrarily paired data, CoDi employs an rext-anchored design, using only text- rext-paired data during its multimodal aligmment stage. This design choice of CoDi makes alignment between non-text modality, such as audio-image align- ment, to be indirectly captured with the aid text. We note that OmniFlow is also implicitly relying on text representation, given the fact that its weights are initialized from pretrained text-to-image and

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Input
Output
Input
Output
The train tracks are lined with bluebonnets.
An oil painting chair.
HN|
(a girl talking)
(Train departing sound)
emergency vehicle siren wails and engine while fire truck horn triggered
quiet garden with colorful flowers blooming.
(A siren and horn sound)
Rain falls heavily
An older man in suit and tie a bouton.
forested area.
(raining sound)
man talking)
(a) many-to-one generation
(b) one-to-many generation
Figure 2: Qualitative results various many-to-men many-to-many generation tasks. More results and compar- isons with baselines are presented in Appendix
text-to-audio models at the beginning of second-stage stage any-to-any training. In contrast, FlowBind learns shared latent space directly from the available paired training data, treating all modalities symmetrically and thereby offering a more suitable representation for cross modal generation.
Train Efficiency While achieving promising performance relative to prior any-to-any generation models, we emphasize that FlowBind is trained with substantially less computation and effort. As previously shown in Table [ the demonstrated strong performance of FlowBind is achieved using times less training parameters and 10 times less compute compared to OmniFlow. Moreover, Flow- Bind employs a unified training objective, in contrast to prior works that require complex multi-stage training pipelines. Consequently, FlowBind be trained with less effort without cumbersome hy- perparameters and additional computations that emerge from those complex training procedures.
Data Efficiency In terms of data efficiency, Flow achieves any-to-any generation with much smaller training dataset, using 0.15 % of CoDi and 1.79 of OmniFlow. We conjecture the train- ing can be done with much smaller dataset because we choose model flow between high-level representations. By doing so, the cross-modal generation capability is decomposed into inter-modal aligmment and intra-modal generation in FlowBind. Our drift network is only required capture modal correspondence, as per-m moralityy frozen encoder-decoders take charge in capturing intra- modal generative capability. This would enable FlowBind quickly capture cross-moda modal alignment with smaller datasets.
5.3 RESULTS MANY-TO-MANT GENERATION
Beyond our extensive one-to-one evaluations, we further conduct qualitative analyses to assess capa- bility FlowBind an any-to-any generation model on more challenging cross-modal generation tasks, complemented by our own quantitative comparisons against strong baselines. As shown in Figure 2 FlowBind handles these complex settings while faithfully reflecting the input conditions in its generated outputs.
Interestingly, we observe that fine-grained details from the imput (e.g., the Stars and Stripes pat- tern printed on the table) can reappear in the generated modalities, as shown in the second row of Fig. a). This suggests that FlowBind's learned shared space expressive enough for cross-modal generation, allowing it to aggregate complementary information from multiple input conditions via latent averaging. We further analyze the effectiveness of this aggregation Appendix E Additional qualitative examples provided in Appendix and our project page.
We also conduct quantitative evaluations of Flow Bind on many-to-on and one-to- toay generation tasks to validate its performance and compare it against baselines. To this end, construct synthetic triplet dataset by extending the AudioCaps (Kim et al.) 2019) text-audio pairs. Following protocol similar to OmniFlow, generate the missing image modality using 1-shell
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Table 4: Many-to-one generation alignment performances.
(I+A) +
(T+A) -I
(T+I) A
Model
CLIP (I (T) CLAP (A +T) CLIP (T -I) AIS (A (A-1I CLAP (T -A) AIS (I-+A)
CoDi
24.04
20.66
25.17
57.52
4.85
61.28
OmniFlow
26.38
36.07
24.06
54.90
7.68
59.32
FlowBind
27.83
35.21
25.57
57.93
28.13
76.02
Table 5: One-to-many generation alignment performances.
(I+A)
1 (T+A)
(T+I)
Model
CLIP (T-1) CLAP (T-A) CLIP (1-1)
AIS (1~A) CLAP (A-()A-T)
AIS (A 1)
CoDi
26.61
10.99
25.73
58.65
18,03
57.14
OmniFlow
24.71
12.92
26.36
63.99
36.07
54.22
FlowBind
25.02
29.12
27.98
74.34
36.79
59.99
Black Forest Labs, 2024), conditioned on the text annotations. This yields triplet (text, image, audio) dataset that enables quantitative evaluation of many-to-many generation.
Tables 4 5 report results for the many-to-one and one-to-many settings, respectively, comparing Flow Bond against other flow-based models. For each setting, we measure alignment scores for all in- put-output modality combinations using the same alignment metries as in the one-to-one evaluation. Note that for all metrics (CLIP, CLAP, AIS), higher values indicate better alignment. Overall, Flow- Bind achieves competitive or superior alignment across tasks. Notably, exhibits substantially reduced tendency to ignore specific conditioning modalities in many- tony-to-ooe settings, demonstrating more balanced cross- modal conditioning. For instance, as shown in Table while CoDi and Omni- Flow tend to disregard the text input during (Text+Image) Audio generation, FlowBind effectively incorporates both modalities.
6 ANALYSIS
Fixed v.s. Learnable Shared Anchor The the- oretical analysis Section 3.1 suggests that our training objective induces meaningful shared la- tent space that serves as an effective anchor. To further support this claim, conduct empir- ical comparison between having text modality as fixed anchor and having learnable, shared la- tent space as an anchor. Similar to the alignment procedure in CoDi, baseline that directly utilizes text modality as a
Table 6: Comparison of alignment scores be- tween model that uses fixed text anchor and learnable shared anchor. I-A represents the image-audio datasct.
Model
1
A
1 A
Text-anchoring
27.94
36.72
55.48
consider text-anchoring
FlowBind w/o I-A
30.04
37.04
61.88
fixed anchor. Since the image-audio pair cannot be used in this setting, we compare text-anchoring baseline with a variant of FlowBind that excludes image-audio pair during training. The resulting data-controlled comparison, as reported in Table [ shows that cross-modal alignment can be im- proved by introducing learned shared latent space. Specifically, FlowBind variant trained without image-audio pair still outperforms text-anchoring variant in all three measured alignment scores. This suggests that employing learnable shared latent space can be beneficial for cross-modal align- ment in general, validating our proposed objective in Eq. 7
Analysis on Shared Latent As mentioned in Section 3.1 our learning objective is designed to produce shared latent representation that unifies information from all input modalities. We ana- lyze the characteristics of the learned space, hypothesizing that it should exhibit strong cross-modal alignment. To quantitatively assess gross-modal alignment of the shared latent space, we measure the CKNNA metric proposed by Huh et al. (2024), comparing alignment in the shared latent space against that in modality-speeiiff encoded latent spaces. We follow the suggested procedure for com- puting CKNNA measure, using most 1024 samples with neighborhood size k set to 10. The analysis is done for text-audio and audio-image alignment, the settings where held-out test set is available.

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As shown in Table 7 our representation from the learn able shared latent space exhibits higher alignment scores compared to the latents that are obtained from per- modality encoders. This quantitatively measured im- provement in alignment validates our claim that the shared latent space is not merely co-embedding of fea- tures, but rather a truly shared semantie space. Our frame- work successfully learns a coherent, well-aligned multi- modal latent space that bridges the semantic gap between different modalities, thereby handling complex any-to-any generation tasks effectively.
Table Shared latent space yields higher alignment measured in CKNNA.
Model
T-A
A-I
Latent
0.1965
0.1343
Shared Latent
0.2872
0.3026
In addition to the quantitative analysis, we conduct a qualitative study of the shared latent space by interpolating between two latents and decoding the latent representations into two different modal- ities, text and image. As shown in Figure [] we empirically observe that the shared latent space is indeed a well-aligned, semantically meaningful space, enabling the semantic of decoded image change gradually between two inputs. Additional analysis, including visualizations of the shared latent space, is provided in Appendix F
kitchen with kitchen with an and kitchen sinks
kitchen with an A room with wooden oven and wooden cabinets and large wooden floors and windows
roon with
kitchen with oven and windows
oven and sink
wandow
large windows
FlowBind's shared latent space learn semantically meaningful space, allowing smooth transition when interpolating between two latents. Data with blue boundary indicates input.
Figure 3:
Extension to Additional Modality To demonstrate FlowBind's scalability beyond text, images, and audio, we extend it to 3D point clouds and train on Pix3D (Sun et al. 2018). Adding this modal- ity requires only additonal modality-specifii drift network, the parameter count grows roughly linearly with the number of modalities N. Crucially, although training uses only an additional paired image-point clouds dataset, FlowBind not only achieves strong performance combinations (Figure (). but also generalizes to unseen cross-modal generation tasks, such as text + point clouds and point clouds + text (Figure 7. This indicates that our central learnable anchor can effectively leverage arbitrarily partially paired data. Detailed settings and qualitative results are provided in Appendix G
the seen modality
CONCLUSION
In this work, we introduce a novel framework for any-to-any multi-modal generation that directly addresses the critical limitations of data scarcity and computational complexity inherent in prior methods. By learning from arbitrarily paired data, our model alleviates the need for impractical fully-paired or anchor-based datasets. The core of our approach is shared latent space trained end- to-end with a single unified flow matching objective. This design not only simplifies the training pipeline but also yields computationally efficient and highly scalable. Our experiments demon- strate that this approach achieves competitive performance, particularly non-text-anchored tasks, and learns well-structured. semantically aligned latent space. Overall, our fata-flexible and effi- cient framework represents a significant step towards building generalist generative models.
Acknowledgments This work was in part supported by the National Research Foundation of Ko- rea (RS-2024-003512) and 20240004366111 the Institute of Information & communications Technology Planning & Evaluation (ITTP) (RS- (RS-2024400500279 I1220959,
RS-2022-1122092 and RS-2022 and the High-Performann Computing Support Project funded by the Korea government (MSIT). We thank Jaehoon Yoo for thoughtful discussions. We also thank Whie Jung, Dong Hoon Lee, Kiet T. Nguyen, Chanryeol Lee, and Wonjung Kim (all with KAIST) for their help with the qualitative evaluation.
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A PROOFS OF EXPECTED CONDITIONAL VARIANCE
Setup. Let E be the number modalities and define the shared latent X = 2"
Holz .... ) Rdx, Fix i { ] and
===2--2 E R f(X) = ugg (X,0) R.
m(X) = B X| = B[2 - x²" |0"].
Assume square integraiitr. B| < oc and E sc. (For vectors, Var(Z X) tr Cov(Z X))
Objective at t=0.
L.(0,)) - Y
Decomposition with orthogonality. Add and subtract mx(X) and expand:
lI(x) r1 ll mx)) [|m((X) 11 + 2(f(X) - m(X), m(X) Y).
Taking expectations and conditioning on X,
[X) - m(X), m(X) - -Y) = [f(((()) (X). (lm(()) -Y [x]}] O.
since ((m - Y x] = m(X) - -EY X = 0. Equivalently,
m(X) YY 1 (x)) and f(X) m(X) E (x(()))).
Thus,
L, 6) =E[IY m(X)1 + (X) mx E Var | |²*]] + E Ug (2", .) E[ - 2" 22" 11
unexplained
distance Bayes
Consequently,
min (,( -E(Var(2* 12)),
attained by
u/ (=",0) E 12 12-1-2 - 2"
Summed objective. For S C {1,.... N), define
(0, = E
Σ
Ug (2", (2 2*)
LiES
Summing the above identity over i E S and using linearity of expectation,
(== = Σ
Var(= xx)]]= + Σ
B Ug (2" 0) E[z - =" 1
EE
hence
min (f=
Σ iES
Var(z [2")], with Ug. '(2".) = E[2" |0" - 2" Vi E
Law of Total Variance
our 1-0 formulation
()) = R Var(= |=*]] + E (=" (=",0) ,0) -E(2-=² |= |=']112]]
unesplained
distance Bayes
there are concrete benefits to reducing it:
Implication. By the Law of Total Variance, Var(=*) = E[Var(>' 2[] + Var(E[2* ==1), so minimizing the unexplained part E[Var(>* 2-)] equivalently maximizes the explained variance Var(E|2* 2*)). Because the decomposition holds for any (0, ø). gradients w.r.t. & (the encoder) continually reduce the summed unexplained variance across modalities.
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B TRAINING AND INFERENCE
This section presents the detailed training and inference algorithms provide clear understanding of each procedural formally.
Algorithm 1: Training
Algorithm 2: Inference
Input : Minibatch {2*
Input Sources with {2) hes: target ji
Aux encoder Hş:
Learned flows
{ ODESOLVE.
Flows {Eg: 1 (params f);
Output: $).
Time sampler p(t).
// Encode sources to shared latent (#:1+0)
Output: Loss C
for each step do Sample {z ) 1==1- for b = to 13 do
for each i S do g f ODESOLVE Ugi, 0):
-n E
L =6 t (z))
// Decode to target (t: 0++1)
Draw la ~ p(l) for 1.... B: C + 0, M 0 for b 1 to B do
2 + ODESOLVE("", upi 0, 1);
return 8
for each E St do
2 (1 2z)2g tb2
i - Ugi (z); 1b);
u* + 2/ 2g:
L <- L + -| u=" 11:: M M
12
13
M > then
L L - L/M;
15
return L
C IMPLEMENTATION DETAILS
C.I ENCODERS AND DECODERS FOR EACH MODALITY
For image, we use CLIP (Radford et al. 2021 for visual latent with Stable-UnCLIP (HuggingFace) 2025) as decoder. For audio, we use CLAP AudioLDM (Liu et al.) 2023). For text, we find that existing text autoencoders such Optimus (Li ct al. 2020 has limited reconstruction abilities. Therefore, we use EmbeddingGemmaa (Team et al.) 2025 for text encoder, and train its decoder with simple reconstruction objective. We pretrained Gemma3-18 (Team et al., 2025) for initialization and finetune it on two epochs of all texts used in encoders and decoders frozen during the training of PlowBind. thereby not counted as trained parameters when reporting trainable parameters.
(Elizalde et al.) 2023) features for conditioning on
Table 8 Note that these
C.2 ARCHITECTURE
We employ Multi-Layer Perceptron (MIP) for both the flow models (ugi J and the joint estimator H AdaLN (Peebles & Xie, 2023) 2023) is applied to all drift networks to improve time modulation. To ensure dimensional consistency for the flow-based formulation, we set the feature dimensionality to 768 across all modalities. For CLAP-based audio features, use lightweight two-layer MLP projection and reconstruction modules to map between the CLAP feature space and our shared latent space. For the auxiliary encoder, each modality input is processed by lightweight modality -speciff modules, and the resulting outputs are averaged. To enhance training robustness, we incorporate a fixed variance term hyperparameter that regularizes the learned representations.
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C.3 TRAINING DATASET
We employ all three types of paired data across text, image and audio. We do not use triplet data in experiments. We summarize the details about training dataset in Table g
Table 8: Training dataset summary.
Type
Dataset name
Size
Description
Subset of LATON eV (2025). filtered by aesthetic scores >> 5.0. Captions synthetrcally generated.
LAION-COCO
242K
Tmage
Flickr-30k
30K
Sentence-hased image descriptions from Plummer et al. 2015
Text-Audio
AudioCaps v2
91K
Natural-language audio captions parsed from YouTube sup- ported by Kim al. (2019)
Audio-Image
VGGSound
184K
Large-scale audio-visual dataset from YouTube supported by Chen al. 2020
C.4 TRAINING RECIPE
We trained the model for 200K iterations using the Adam optimizer and a global batch size of 1024. The total training process requires approximately 48 GPU-hours on NVIDIA H100. To train each drift network, normalized the latent representations of each modality match their respective scales. During training. following Kim et al.] (2024), we apply the endpoint (i.e., t = 1) velocity prediction objective with probability 0.3, which empirically improves stability.
D EVALUATION SETUP
We evaluate both generation quality and cross-modal alignment using standard, modality- appropriate metrics. Generation quality is measured with FID (Heusel et al. 2017) for images, FAD (Kilgour et al., 2019) for audio, and CIDEr (Vedantam et (015) for text captions. Cross- modal alignment is quantified via pairwise similarity scores: CLiPScore for text- image pairs (Hessel al. 2021). CLAPScore for rext-audio pairs (Elizalde et al. (003). and Audio Image Similarity (AIS) for image-audio pairs (Wu et al. 2022). For text-audio and image-audio tasks, we report results on held-out test splits, whereas for text-to-image and image-to-text we follow the widely adopted MS-COCO zero-shot evaluation protocol.
We follow a specialist- protocol, selecting strong off-the-shelf models for each modality pair: SD3-Medium (Esser et al., (204)) (also used the OmniFlow text-to-image backbone) and FLUX.1 (Black Forest Labs] 2024) for text-to-image; LLaVA-NeXT (Li al., 2025a) for image-to- text; TangoFlux (Hung et al. 2024) and AudioX (Tian et al.) 2025) for text-to-audio; Qwen2-Audio
(Chu et al. 2024) for audio-to-text; Seeing& Hearing (Xing et al. 2024) for image-to-audio; and
Sound2Vision (Sung-Bin et al. 2024 for audio-to-image.
Madiio-ImageSSimilatt (AIS) We followed SonicDiffusion (Biner al. 2024 to measure rela- tive AIS on audio-image evaluations. In contrast to other alignment metrics, AIS is reference-batt metric to compensate different scales of measured cosine similarity. For audio-to-image evaluation, AIS is defined as ratio of audios in testset that achieve worse cosine similarity than conditioning audio, measured with genrated image. For example, AIS zero the generated image is not aligned at all and thus shows least cosine similarity among test set audios. The similarity is measured using wav2clip (Wu et al., 2022) audio embedding and image CLIP (Radford et al. 2021) embedding from ViT-B/32 model. We generalize AIS metric for image-to-aue au-dooio generation in symmetric way, counting the ratio of images in test set that gives lower cosine similarity compared to conditioning image.
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E ROBUSTNESS OF PLAIN AVERAGING
In this section, we analyze FlowBind's robustness to competing source modalities in many-to-one generation. We construct conflict set by randomly pairing audio clips with text prompts that deliberately describe different semantics. We then performed (T A) + generation with plain averaging in the shared latent space, and present the results Figure
quiet alleyway night.
A rooftop terrace with view.
A cozy living
A forest path.
A city street garden
with buildings. with flowers.
(Food cooking)
(Cat meowing)
(Water flowing)
(Train passing)
(Cello playing)
(Animal crying)
Figure 4: Results on conflicting conditions of (text+audio) ) -to-image generation.
In this challenging setup, FlowBind faithfully reflects the two conflicting conditions most cases, rather than collapsing an incoherent blend or ignoring one modality.
We attribute this robustness to the shared latent space learned by FlowBind.. As shown Table 7 the shared latent exhibits strong cross modal alignment. We conjecture that structured and semantically coherent geometry our shared latent space enables even simple averaging to yield stable and meaningful behavior under conflicting conditions.
F FLOWBIND LATENT VISUALIZATION
In this section, further analyze the shared latent space by visualizing the relationship between latent representations and generated content. Specifically, we present a t-SNE visualization of Flow- Bind's shared latent space together with representative generated images.
We sample 5,000 text prompts from the MS-COCO evaluation set, encode each prompt into Flow- Bind's shared latent space, and cluster the resulting latent vectors using A-NN with k=15 (Fig- ure (a). For five selected clusters, we decode the five latent vectors closest to each cluster center into images (Figure(5b) 5b).
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20-
10
-10
20
(a) I-SNE visualization shared latent space using MS-COCO captions. Clusters are formed by k-NN with = 15.
(b) Example image clusters decoded from latent points within randomly selected cluster. Each color boundary represents distinct cluster, as shown in 5a
Figure 5: Visualization of shared latent space of FlowBind and corresponding generated images.
The examples in Figure 5b show that samples drawn from the same cluster in the shared latent space are semantically coherent (e.g., office scenes, tennis players, food dishes, trains, animals), while different clusters capture clearly distinct concepts. These results support that our shared latent space forms representations according to high-level semantics, so that nearby latent points correspond to consistent and meaningful variations in the generated contents.
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G FLOWBIND WITH ADDITIONAL MODALITY
To demonstrate the scalability of FlowBind, we extend our framework to an additional modality, namely 3D point clouds. We use the Pix3D dataset (Sun al. 2018), which contains 10k pairs of (Image, Point cloud), and adopt a pre-trained modality-specific autoencoder from (Yang et al.) 2019). All other settings are kept the same as in our main experiments (Section [5:; adding new modality only introduces its speeiit drift network, leading to approximately linear growth in the total number of parameters.
Figure 6 presents the qualitative results for cross-modal generation of image-point clouds, demon- strating strong performance while preserving the geometry the underlying object and overall consistency of the shape.
More importantly, as shown in Figure 7 FlowBind also achieves reasonable performance on unseen cross-modal combinations (e.g., text point clouds and point clouds - text), indicating that framework can effectively exploit arbitrarily partially paired data, owing to its central learnable anchor design.
Input
Output
Input
Output
(a) Results on point clouds to-image generation
(b) Results on image-to to--toottttttt clouds generation
Figure Cross modal generation results on clouds
Input
Output
Input
Output
The chair has pillow in grey fabric.
a wooden chair
The daylight table with bowls
the bookcase
with books in it
(a) Results on point clouds-to-text generation
(b) Results on text-to-point clouds generation
Figure 7: Cross-modal generation results on text-point clouds. FlowBind handles cross-modal gen-
erations unseen during training by effectively leveraging arbitrarily partially paired data.
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H QUALITATIVE RESULTS
In this section, present more qualitative results various any-to-any generation, including
to-one (Figures & 11 one-to-many (Figures (2)13), and many-to-one (Figures [6) generation.
Qualitative results various input and output modality combinations are provided in our project
page: ot wtt.fiiiiiiooo.fiiilal.l.a..aa.a lfciiallllollll flobind
shows that FlowBind faithfully translate input modalities into another modalities while preserv- ing content. Compared to baseline, FlowBind exhibits stronger qualitative results especially on challenging many-to-one generation tasks. Specifically, observe that the baselines struggle to preserve the heterogeneous contents of different modalities, often failing to produce content of one of two modalities. Compared to this, FlowBind faithfully generates outputs that preserve the content of all input modalities, showcasing the advantage of Bind in any-to-any generation tasks.
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Conditions
FlowBind
CoDi
OmniFlow
A busy street with several cars and buses driving on it.
TYE Cr
The baseball player is throwing the ball hard.
88
There are many teddy bears sitting the shelves.
group of biker turning into a curb
A white horse standing next to a tree.
Meat and broccoli in sauce are in a bowl.
Figure 8: Results on text-to-image generation.
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Conditions
FlowBind
CoDi
OmniFlow
A monkey is sitting top of a rock eating some food.
monkey on a little girl eating banana monkey
a man feeding baby chimpanzee with spoon
a group of people and children sitting at a beach with several trucks parked nearby
People at the beach in the sand are gathered on a sunny day.
three men are beach at the beach home
A man and woman in formal attire standing next to each other.
couple and dress wear an opera and an attractive man
man and woman standing together while he is kissing her
A plate with a pizza and food on top.
pizza and tomato, turkey and pizza
plate of fires and piece of chicken on plate
new bathroom chairs get messy so bathroom rooms have bathroom colors
A bathroom with tile and sink, and a mirror above the wall.
bathroom with wall-mounted garden tray
young boy is holding a red toy on the beach as adult looks on.
two boys sitting on surfboard with yellow kite
boy gets his boat while for the swim.
Figure 9: Results on image-to-text generation.
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Conditions
FlowBind
CoDi
OmniFlow
Birds vocalize chirping while man speaks
Man singing for the bird in the tree
A man speaks and a duck quacks
(A man talking as birds are chirping)
Water reflection person bathroom in bathroom
A toilet flushes and water drains
A cat meows followed by thump
(A toilet flushing)
Firefighter working on stick is sharp in the fire
A loud power tool grinding and drilling
A machine is being used
(Humming and vibrating of a tool)
A person snoring while sleeping and someone speaks
Two men sleeping and watching the other in the night
A person snoring
(A person snoring and man talking)
driver fleeing for a left turn
boat engine starting then running
speeding to chase a red horseback from the speeding car
A frog croaks nearby
(A motorboat engine turns on)
a pedestrian walking by a train city street while on a city sidewalk walking in the city, where traffic is city.
traffic is passing with some distant vehicle sirens going off
A siren is going off and helicopters are flying
(Light music playing followed by police sirens)
Figure 10: Results on audio-to-text generation.
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Conditions
FlowBind
CoDi
OmniFlow
(A women talking)
(Animal crying with water splashing)
(Machine running
(A bird chirping)
(Train sound)
Figure 11: Results on audio-to to-image generation.
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Condition
HlowBind
CoDi
OmniFlow
(A Train passing by, horning)
train clanks its wheels as it passes by followed by train whistle
railroad train at.
A drill is fired
(A woman speeching)
woman says to woman who is speaking in her speech at speech she has told her.
A woman talking and whispering
woman giving a speech
(A siren and horn sound)
emergency vehicle siren wails and engine runs while fire truck horn triggered
four cars passing for police to go back
A siren wails and police car passes by
south after fire but come opposite car speeds.
Figure 12: Results on audio-to-(text, image) generation.
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Condition
FlowBind
CoDi
OmniFlow
The train tracks are lined with bluebonnets.
Holf -fmont /
(Train departing sound)
(Rustling sound)
(Clashing iron)
A cat is sitting on the sofa.
(cat meowing)
(rattling sound)
(keyboard tapping)
A dog is sitting on a couch and barking.
(Dog barking)
(Child laughing and (Dog barking) screaming)
Figure 13: Results on eex-to-((imaee-audio generation.
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Input Conditions
FlowBind
CoDi
OmniFlow
cute dog playing in the park.
(a siren ringing)
A car speeding down a highway
(various animal sound)
A misty forest path.
(choo-choo sound of train)
A living room with carpet
(playing the cello)
A garden with flowers.
(water flowing)
Figure 14: Results on (text+audio) sactaauddoo----iiinee -to-image generation.
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Conditions
FlowBind
CoDi
OmniFlow
A bell rings while motorcycle grooms.
horseback riding with her woman
wind blows through the microphone as horse gallops
(A bell ringing)
Beliere
city street at night with Christmas lights and fireworks display.
The birds tweet on the building, after they sing
beer is pouring wine for party.
(Birds tweeting)
OCJ
An old time photo shows car horn blows while man
black and white painting of steam locomotive with man standing next to it
blue woman reading with train next.
(A vehicle honking)
soccer players celebrate after they won lisa 's goal after their parents left the soccer match the français tfr their
France players celebrating with their team after a victory
A group of boys cheer and scream
(A man screaming)
child living room with couches and children
boy doesn't t appear in the house, that day.
child's room with couch, table and book shelf.
(A child talking)
Figure 15: Results on (imago+audio)-to-- -to-txxx generation.
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Input Text: sunset over ocean, casting orange and pink hues across the sky.
Input Image
FlowBind
CoDi
AEROMEXI
00000
Input Text: A quiet garden with colorful flowers blooming.
Input Image
FlowBind
CoDi
Figure 16: Results on {tex--imagee-to--mmgg generation.
30


Reviews:
[{"review_id": "aggaGo3XZU", "reviewer": "Reviewer_kcBk", "summary": "The paper proposes FlowBind, a flow‑based any‑to‑any generative framework that replaces a fixed Gaussian prior with a learnable, shared latent “anchor.” Each modality connects to this anchor via its own invertible drift network; all drifts and the auxiliary encoder producing the shared latent are trained jointly with a single flow‑matching objective. At inference, per‑modality ODEs are integrated backward to the shared latent and forward to the target modality, enabling many‑to‑many translation. The implementation freezes modality encoders/decoders (EmbeddingGemma for text, CLIP+Stable‑UnCLIP for images, CLAP for audio) and uses small MLP drifts.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- [S1] The idea is simple and easy to follow.  \n- [S2] Promising efficiency: strong results with a lightweight model, fewer GPU‑hours than previous works.\n- [S3] Competitive results on several quantitative measurements (e.g., lower FID for T→I and A→I than baselines).", "weaknesses": "- [W1] Insufficient ablations for core claims. The main “analysis” section contains only two small studies: fixed text‑anchor vs learnable shared anchor (Table 4) and a CKNNA alignment probe (Table 5). There are no ablations on (i) the shared‑latent aggregation rule (simple averaging vs alternatives such as weighted averaging, or learned averaging), (ii) latent dimensionality or encoder/decoder choices, or (iii) the fraction of partially paired data. Without these, it is hard to attribute gains to the proposed factorization rather than to frozen backbones or evaluation choices.  \n- [W2] Partial‑pairing claim not quantified. The paper argues for learning from arbitrary partially paired data, but does not vary the paired/unpaired ratios or show robustness curves. This leaves the “data‑flexible” claim largely untested.  \n- [W3] Many‑to‑many evaluation is qualitative only. This use‑case is shown via figures and a website but lacks quantitative measurements.  \n- [W4] Mixed performance is under‑discussed. FlowBind underperforms OmniFlow on some alignment metrics (e.g., T→I CLIP), while excelling on others (e.g., AIS, CLAP); the paper does not analyze why or when factorization helps/hurts. More explanations are needed. \n- [W5] (Minor) Cost comparison caveat. The reported 10× compute reduction (48 vs 480 GPU‑hr) compares FlowBind’s full training to OmniFlow’s final joint stage only. Given the also‑much‑smaller data used by FlowBind, the speedup is promising but not an apples‑to‑apples pipeline cost.", "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": "a8NfhP2wO3", "reviewer": "Reviewer_Hcpe", "summary": "This work replaces the usual logic of any-to-any generation, “everything must talk to text” or “one giant joint flow over all modalities”, with a learnable shared latent and one invertible flow per modality, all trained with one flow-matching loss. On text–image–audio cross-modal generation, they beat/are competitive with CoDi/OmniFlow on 6 one-to-one tasks, especially strong on image-audio, while using 6× fewer params and ~10× less compute than OmniFlow (568M, 48 GPU-hr on H100).", "questions": "Right now you average the per-modality latents. Did you try learned fusion (attention / confidence weighting) and does it improve conflicting-condition cases (e.g. image says “dog” but audio says “car”)?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. One shared latent + per-modality flows = no quadratic blow-up over modalities, unlike joint-time rectified flows.\n2. No “align to text first, then joint train” and also no post-merge stage. This offers engineering advantage.\n3. Each flow only needs its modality + latent, so they can actually use image–audio pair easily.\n4. Simple averaging in latent still gives multi-condition outputs", "weaknesses": "1. For competing source modalities (conflicting audio + image like two contradicting semantics), how robust is plain averaging?\n2. This work focuses more on single cross-modal generation not joint-outputs generation.\n3. ODE cost at inference is per modality. There’s still ODE solves on both sides. And there is not enough report on runtime / steps / efficiency as the main claim of this framework is efficiency.", "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": "bl1PURRFOd", "reviewer": "Reviewer_XtMR", "summary": "This paper introduces FlowBind, a flow-based generative model designed for efficient any-to-any multimodal generation. The core innovation lies in the introduction of a learnable shared latent space that captures cross-modal commonalities, enabling each modality to connect to this latent via its own invertible flow. The model is trained end-to-end under a single flow-matching objective, eliminating the need for multi-stage pipelines or fully-paired datasets. Experiments across text, image, and audio modalities demonstrate that FlowBind achieves competitive performance with significantly reduced computational and data requirements compared to existing baselines like CoDi and OmniFlow.\nThe contribution can be summarized as:\n1. FlowBind enables any-to-any generation by factorizing multimodal interactions through a shared latent space, reducing computational complexity and supporting training with partially paired data.\n2. The model jointly optimizes the shared latent and modality-specific flows under a single objective, avoiding complex multi-stage training pipelines.\n3. FlowBind achieves strong performance with only 568M trainable parameters, 48 GPU-hours of training, and a fraction of the data required by prior methods.", "questions": "see Weaknesses", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-structured, clearly written, and easy to understand. The idea of a learnable shared latent as a dynamic anchor for multimodal flows is both solid and well-motivated, addressing limitations of fixed anchors and joint conditioning models.\n2. The evaluation covers six one-to-one cross-modal tasks (text-image, text-audio, image-audio, and their reverses) and complex many-to-many generation, with rigorous metrics (FID, FAD, CIDEr, CLIP, CLAP, AIS) for both quality and alignment. The qualitative results (e.g., preserving fine-grained details in multi-modal inputs) effectively showcase the framework’s expressiveness.\n3. The empirical results clearly demonstrate FlowBind’s advantages: requiring up to 6× fewer parameters, training 10× faster, and using less than 10% of the data while achieving competitive or better generation quality. The single-stage training pipeline (avoiding multi-stage alignment/generation decoupling) further enhances its practicality for real-world scenarios.", "weaknesses": "1. While the paper empirically demonstrates that FlowBind outperforms larger models trained on more data, it lacks a thorough and detailed analysis explaining why this is the case. The authors should provide a deeper investigation into the factors contributing to this unreasonable efficiency. \n2. Relying on CLIP (and analogous pre-trained encoders) introduces inherent limitations in capturing fine-grained details, directly constraining generation accuracy. CLIP’s training is driven by coarse-grained semantic alignment between images and text. This may lead to systematic loss of fine visual patterns—such as object positions, subtle textures, or local structure variations.\n3. Although the paper includes a basic interpolation analysis (Figure 3) and alignment metrics (Table 5), it lacks a comprehensive interpretability analysis of the learned shared latent space. The authors should provide a visualization of the relationship between the latents and generated contents.\n4. (Not Important) The experiments are limited to text, image, and audio. The paper does not address how FlowBind would scale to additional modalities. Questions remain about potential bottlenecks in the shared latent space or computational cost as the number of modalities grows.", "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": "LON8rx9WIg", "reviewer": "Reviewer_1rzN", "summary": "This paper introduces FlowBind, a novel framework for any-to-any multimodal generation. The core idea is to learn a shared latent space that captures cross-modal semantics, and to connect each modality to this space via a dedicated, invertible flow model. The shared latent and the modality-specific flows—are trained jointly under a single flow-matching objective, which allows the model to learn from arbitrary data pairings (e.g., text-image, image-audio) in a unified, single-stage training process.\n\nThe authors claim this design offers significant advantages in efficiency and data flexibility. At inference, the invertible flows function as encoders and decoders, enabling direct translation between any subset of modalities by first mapping the source modality to the shared latent space and then mapping from the latent space to the target modality. The paper presents experiments on text, image, and audio generation tasks, arguing that FlowBind achieves competitive performance against prior methods like CoDi and OmniFlow, while requiring substantially fewer parameters and less training computation.", "questions": "1.  **On the Choice of Baselines**: My primary concern is that the baselines used for comparison are not representative of the current state of the art, making it difficult to assess the true performance of FlowBind. Could you provide a justification for this choice? More importantly, to better situate your results, could you provide comparisons against more recent and powerful models, even on a subset of key metrics? For example:\n    *   **Text-to-Image**: How does FlowBind compare to recent diffusion models like **FLUX.1[1]**?\n    *   **Image-to-Text**: How does it perform against current vision-language models like **LLaVA-NeXT[2]**?\n    *   **Text-to-Audio**: How does it stack up against state-of-the-art audio generators like **Tangoflux[3]**, or **AudioX[4]**?\n    *   **Audio-to-Text**: How does its performance compare to captioning systems built on top of strong speech models like **Qwen2-audio**?\n\n2.  **On the Contribution of the FlowBind Framework**: The method relies heavily on very powerful frozen pre-trained models (Gemma, CLIP, Stable-UnCLIP decoder, etc.). This makes it difficult to disentangle the performance gains from your novel flow-based alignment versus the strong priors inherited from these components. To better isolate the contribution of your framework, could you provide an ablation study that uses different encoders/decoders? This would help clarify how much of the final quality is attributable to the learned flow itself.\n\n3.  **On the Scalability of the \"Any-to-Any\" Claim**: The experiments are limited to three modalities. While the framework is designed to be general, have you performed any preliminary experiments or analysis on how FlowBind would scale to a larger set of modalities, such as video?\n\n[1] Labs B F, Batifol S, Blattmann A, et al. FLUX. 1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space[J]. arXiv preprint arXiv:2506.15742, 2025.\n\n[2] Li F, Zhang R, Zhang H, et al. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models[J]. arXiv preprint arXiv:2407.07895, 2024.\n\n[3] Hung C Y, Majumder N, Kong Z, et al. Tangoflux: Super fast and faithful text to audio generation with flow matching and clap-ranked preference optimization[J]. arXiv preprint arXiv:2412.21037, 2024.\n\n[4] Tian Z, Jin Y, Liu Z, et al. Audiox: Diffusion transformer for anything-to-audio generation[J]. arXiv preprint arXiv:2503.10522, 2025.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "*   **Novel and Elegant Framework Design**: The paper proposes a conceptually clean and novel framework for any-to-any generation. The idea of factorizing the complex joint distribution of multiple modalities through a learnable shared latent space, connected by modality-specific invertible flows, is an elegant and original approach to this problem.\n\n*   **Efficiency and Training Simplicity**: A strength of FlowBind is its computational and data efficiency. By design, the framework avoids the quadratic complexity of joint modeling and simplifies the training process into a single stage with a unified objective. The reported reductions in parameter count and training time are substantial, which is a valuable contribution.\n\n*   **Clarity of Presentation**: The paper is well-written and the core concepts are explained with notable clarity. The illustrations provide an intuitive and effective overview of the training and inference processes, making the proposed methodology easy to follow and understand.", "weaknesses": "Despite its novel framework, the paper's experimental evaluation has several weaknesses that make it difficult to accurately assess its claimed \"competitive\" performance in the context of the current state of the art.\n\n1.  **Outdated and Insufficient Baselines**: The primary weakness of this work is the choice of baselines for comparison, many of which are no longer representative of the state-of-the-art. For instance, in the \"Specialists\" category of Table 2 and 3, **SD3-Medium** was released over a year ago, and **BLIP2** over two years ago. Similarly, audio models like **AudioLDM-L-full** and **WavCaps** are also established prior work. While comparing to \"Generalists\" like CoDi is relevant, the rapid progress in the field means that a truly competitive evaluation must include more recent and powerful models. The lack of comparison to any contemporary MLMMs or recent foundation models makes it impossible to gauge how FlowBind's efficiency-focused design trades off against the generation quality achieved by top-tier systems. This omission leaves a significant gap in the evaluation.\n\n2.  **Limited Scope of Modalities and Tasks**: The experiments are confined to only three modalities (text, image, audio) and primarily focus on one-to-one generation tasks.\n    *   While the framework is presented as \"any-to-any,\" its true scalability and effectiveness on a larger set of modalities (e.g., video) remain unproven.\n    *   The qualitative examples for many-to-one or one-to-many generation (e.g., Figure 2) are interesting but lack quantitative evaluation or strong baseline comparisons, making it difficult to assess performance on these more complex tasks which are central to the \"any-to-any\" promise.\n\n3.  **Contribution Confounded by Frozen Pre-trained Models**: The framework's true contribution is obscured by its heavy reliance on powerful, frozen pre-trained models such as EmbeddingGemma, CLIP, CLAP, and the Stable-UnCLIP decoder. This design effectively reduces the core learning task to aligning the latent spaces of these pre-existing expert models, rather than learning the full generative process. Consequently, the high generation quality reported may be largely inherited from the strong generative priors of these frozen components, which makes it difficult to isolate and evaluate the novel contribution of the FlowBind framework itself.", "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)