Title: LABridge: Text–Image Latent Alignment Framework via Mean-Conditioned OU Process
Venue: NeurIPS 2025
Accepted: yes
Keywords: diffusion

alignment

OU Process
Abstract: Diffusion models have emerged as state‑of‑the‑art in image synthesis.However, it often suffer from semantic instability and slow iterative denoising. We introduce Latent Alignment Framework (LABridge), a novel Text–Image Latent Alignment Framework via an Ornstein–Uhlenbeck (OU) Process, which explicitly preserves and aligns textual and visual semantics in an aligned latent space. LABridge employs a Text-Image Alignment Encoder (TIAE) to encode text prompts into structured priors that are directly aligned with image latents. Instead of a homogeneous Gaussian, Mean-Conditioned OU process smoothly interpolates between these text‑conditioned priors and image latents, improving stability and reducing the number of denoising steps. Extensive experiments on standard text-to-image benchmarks show that LABridge achieves better text–image alignment metric and competitive FID scores compared to leading diffusion baselines. By unifying text and image representations through principled latent alignment, LABridge paves the way for more efficient, semantically consistent, and high‑fidelity text to image generation.

LABridge: Text-Image Latent Alignment Framework via Mean-Conditioned OU Process

Abstract

Diffusion models have emerged as state-of-the-art in image synthesis.However, it often suffer from semantic instability and slow iterative denoising. We introduce Latent Alignment Framework (LABridge), a novel Text-Image Latent Alignment Framework via an Ornstein-Uhlenbeck (OU) Process, which explicitly preserves and aligns textual and visual semantics in an aligned latent space. LABridge employs a Text-Image Alignment Encoder (TIAE) to encode text prompts into structured priors that are directly aligned with image latents. Instead of a homogeneous Gaussian, Mean-Conditioned OU process smoothly interpolates between these text-conditioned priors and image latents, improving stability and reducing the number of denoising steps. Extensive experiments on standard text-to-image benchmarks show that LABridge achieves better text-image alignment metric and competitive FID scores compared to leading diffusion baselines. By unifying text and image representations through principled latent alignment, LABridge paves the way for more efficient, semantically consistent, and high-fidelity text to image generation.

1 Introduction

Diffusion models [Sohl-Dickstein et al., 2015, Song and Ermon, 2019, Ho et al., 2020, Song et al., 2021] represent a significant advancement in generative modeling, demonstrating state-of-the-art performance in diverse tasks such as text generation [Li et al., 2022, Wu et al., 2023], high-fidelity image synthesis [Rombach et al., 2022, Ramesh et al., 2022, Ho et al., 2022, Shao et al., 2023, Lin et al., 2025], image restoration [Blattmann et al., 2023, Brooks et al., 2024], and 3D content creation [Liu et al., 2023, Evans et al., 2024]. These models typically operate by defining a forward diffusion process that gradually adds noise to data, transforming it into a simple prior distribution (often Gaussian), and then learning a reverse process to generate data by iteratively denoising samples drawn from this prior. The mathematical foundation often relies on stochastic differential equations (SDEs) [Song et al., 2021] or discrete-time Markov chains, enabling powerful sampling and manipulation strategies like accelerated generation [Mei et al., 2024], timestep analysis, and model distillation [Shao et al., 2025, Xie et al., 2024, Nguyen et al., 2024, Kang et al., 2024].

Existing Challenges

Despite their success, standard diffusion models face limitations, particularly in text-to-image generation, stemming largely from their reliance on a fixed, often unstructured prior:

Instability and Ambiguity from Homogeneous Priors: Conventional methods map the entire diverse data distribution q data ( x ) to a single, fixed prior, typically N ( 0 , I ) . This forces distinct semantic concepts (e.g., "cat," "dog," "landscape") onto the same simple latent structure. This collapsing of priors can lead to instability, as the reverse process must disentangle these varied

Distribution

(a) Diffusion Sampling Process

Prior

Prompt: 'A cat'

Overlap

Distribution

(b) LABridge Sampling Process

Cat Prior

Cat Region

'A cat'

Dog Region

No Overlap

Dog Prior

'A dog'

semantics from a homogeneous starting point. Furthermore, it can create ambiguity in score estimation ( ∇ x t log p ( x t ) ), as paths originating from different initial data points might overlap significantly in the latent space near t = T , making the learned score an average that lacks precision for any specific semantic direction. We provide analysis in Appendix E.3

Inefficient Sampling: As shown in Fig. 1 (a), the diffusion sampling process follows a curved path. Without strong guidance, especially in early steps, the process can be slow, requiring many iterations (NFE - Number of Function Evaluations) to converge to a high-fidelity image that accurately reflects the conditioning. We provide analysis in Appendix D.3

Weak Text-Vision Alignment: While conditioning mechanisms inject textual information, the fundamental diffusion process still operates between the image manifold and Gaussian prior. This indirect connection can limit the precise alignment between the generated image and complex or nuanced text prompts, especially for out-of-distribution concepts (analysis in Appendix D.2).

Our Innovations: LABridge To overcome these challenges, we introduce LABridge, a framework designed to enhance text-vision alignment and accelerate sampling in diffusion models. LABridge leverages two core ideas: a dedicated encoder TIAE to create structured, text-conditioned priors, and an OU diffusion process to connect image latents directly to these priors.

Text Encoder for Structured Priors: We employ a encoder to process text prompts ( y ) and generate corresponding latent representations µ T ( y ) . The latent µ T ( y ) acts as a structured, textspecific prior mean in the aligned latent space of an image autoencoder. The TIAE ensures that semantically similar texts map to nearby priors, preserving semantic structure.

OU Diffusion Process for Alignment and Stability: We adopt OU process explicitly models the stochastic path between the image latent x 0 (obtained from a VAE encoder) and a distribution centered around the text-specific prior µ T ( y ) , i.e., N ( µ T ( y ) , σ 2 T I ) . The OU process, known for its mean-reverting property, naturally pulls the state towards the target mean µ T ( y ) , inherently promoting stability and alignment.

LABridge offers a new perspective: it frames text-to-image generation as learning a stochastic process between the image latent manifold and a manifold of text-conditioned priors. This explicit alignment via the process mechanism enhances semantic consistency and, by providing a better starting point and direction, significantly speeds up the sampling process.

In summary, our contributions are threefold:

We propose LABridge, a novel framework utilizing an encoder TIAE to generate structured text-conditioned priors µ T ( y ) and an OU diffusion process to align them with image latents x 0 .

We demonstrate that the OU process mechanism, combined with structured priors, improves text-vision alignment and inherently accelerates sampling by providing a more certain trajectory, supported by theoretical analysis.

We validate LABridge experimentally, showing significant improvements in sampling and textimage consistency metrics while maintaining competitive image fidelity compared to baselines.

2 Preliminaries

In this section, we introduce the essential background concepts. Additional technical details, and extended definitions are deferred to Appendix A.

2.1 Diffusion Processes and Score Matching

Diffusion Process. Let x ∈ R d follow the data distribution q data ( x ) . A forward diffusion process defines a sequence of latent variables { x t } t ∈ [0 ,T ] starting from x 0 ∼ q data ( x ) and evolving towards a simple prior distribution p T ( x T ) = N ( 0 , I ) as t goes from 0 to T . This evolution is often described by a stochastic differential equation (SDE) [Song et al., 2021]:

where f ( · , t ) is the drift function, g ( t ) is the diffusion coefficient, and w t is a standard Wiener process. Generating new data involves reversing this process. The corresponding reverse-time SDE is given by [Anderson, 1982]:

where ¯ w t is a Wiener process running backward in time, and p t ( x t ) is the marginal probability density of x t . The crucial term is the score function ∇ x t log p t ( x t ) .

Score Matching. In practice, the true score ∇ x t log p t ( x t ) is unknown and is approximated by a time-dependent neural network s θ ( x t , t ) , often conditioned on additional information y (like text embeddings), denoted s θ ( x t , t, y ) . This network is trained by minimizing a score matching objective [Hyvärinen and Dayan, 2005, Vincent, 2011]. For many diffusion processes (like VP and VE), the conditional score ∇ x t log p t ( x t | x 0 ) is tractable. A common training objective is:

where p t ( x t | x 0 ) = N ( x t ; α t x 0 , σ 2 t I ) defines the transition kernel, and λ ( t ) is a weighting function. For this Gaussian kernel, ∇ x t log p t ( x t | x 0 ) = -( x t -α t x 0 ) /σ 2 t = -ϵ /σ t . This leads to the widely used noise prediction objective:

where ϵ θ is the network predicting the noise ϵ , related to the score network by s θ ( x t , t, y ) = -ϵ θ ( x t , t, y ) /σ t .

2.2 Diffusion Bridge Models

While standard diffusion maps data to a fixed prior, a diffusion bridge connects two specified endpoint distributions, p 0 ( x 0 ) and p T ( x T ) , which can both be complex. This is particularly relevant for tasks involving paired data ( x 0 , x T ) ∼ q data ( x 0 , x T ) , such as image translation or, in our case, aligning image latents x 0 with text-derived priors µ T .

Stochastic Bridges via h -Transform. Given a forward SDE Eq. (1), Doob's h -transform provides a way to condition the process to start at x 0 at t = 0 and end exactly at x T = y at t = T . The resulting bridge SDE is:

where p T | t ( x T | x t ) is the transition probability density of the original SDE from time t to T . The term h ( x t , t, y , T ) = ∇ x t log p T | t ( y | x t ) is the "guidance" term ensuring the endpoint constraint. For linear SDEs with Gaussian transitions (like VP, VE, OU), this term is often tractable.

Denoising Diffusion Bridge Models [Zhou et al., 2023]. Instead of exact endpoints, we often want to sample from a conditional distribution q ( x 0 | x T ) given paired data ( x 0 , x T ) ∼ q data. This can be achieved by reversing a bridge process designed such that its marginals q ( x 0 , x T ) approximate q data ( x 0 , x T ) . Zhou et al. [2023] show that the reverse SDE for sampling x t given x T = y is:

where ∇ x t log q t ( x t | x T = y ) is the score of the conditional bridge distribution.

2.3 Mean-Conditioned Ornstein-Uhlenbeck Process

Ornstein-Uhlenbeck (OU) Process. The OU process is a mean-reverting stochastic process often used to model systems returning to equilibrium. Its SDE is:

where θ > 0 is the rate of mean reversion, µ is the equilibrium mean, and σ is the volatility. The drift term θ ( µ -x t ) pulls the state x t towards µ .

Ornstein-Uhlenbeck Bridge (OUB). An OU process is a conditioned OU process that starts at x 0 at t = 0 and ends at x T = y at t = T . Its SDE can be derived using Doob's h -transform. For the standard OU process with σ = 1 starting from x 0 at t = 0 , the transition density to time T is Gaussian: p T | 0 ( x T | x 0 ) = N ( x T ; µ +( x 0 -µ ) e -θT , σ 2 2 θ (1 -e -2 θT ) I ) . The OUB derived from this inherits the mean-reverting property but ensures the endpoint constraint. In our work, we use a specific form of OU process connecting x 0 to a distribution around µ T ( y ) , defined by the transition:

This corresponds to an OU process SDE d x t = θ ( µ T -x t ) dt + σd w t . This structure is key to LABridge. The parameter θ controls the strength of reversion towards the text-conditioned prior mean µ T . OUB processes can encompass VP and VE under specific parameter choices.

3 Proposed Method: LABridge

3.1 Motivation: Structured Priors and Directed Diffusion

A primary challenge lies in directly using text as latent representations . Text data is fundamentally different from typical latent variables. It is inherently infinite and unstructured ; the space of possible sentences and meanings is vast and does not easily map to predefined, discrete categories or a simple, low-dimensional manifold. Converting raw text directly into a structured latent code suitable for generative models is non-trivial.

Instead, we must first represent text in a suitable format, typically through powerful pretrained embedding models (like CLIP, T5) that capture semantic meaning in high-dimensional vectors ( E y ). However, simply using these embeddings as conditioning signals for a standard diffusion process (mapping image I to noise ϵ ) still relies on the model implicitly learning the complex relationship between the text embedding space and the image manifold during the denoising process initiated from a generic prior.

This motivates the need to represent text in a continuous latent space that is explicitly aligned with the latent space of images . Rather than mapping images to generic noise, we propose mapping images x 0 to text-conditioned priors µ T ( y ) that live in the similar latent space. This requires:

A mechanism to map text embeddings E y to target latent priors µ T ( y ) .

Ensuring these priors µ T ( y ) are semantically consistent (similar texts map to nearby priors) and aligned with corresponding image latents.

A generative process that efficiently connects these aligned endpoint distributions.

Text Latent

Image Latent

Cat

Diffusion

Dog

Continuous text

Corresponding

prior latent

image latent

distribution

distribution

Image

Encoder

Image

Latent

Raw Image

TIAE

Text Embedding

Model

pretrained & frozen

Text

Text Embedding

Text

Latent

Diffusion Model

3.2 LABridge Framework Overview

As depicted in Fig. 3, LABridge integrates three key components:

Pretrained Image Autoencoder: We utilize a frozen Variational Autoencoder (V AE) with encoder E VAE and decoder D VAE. The encoder maps an input image I to a latent representation x 0 = E VAE ( I ) in R d , effectively moving the diffusion process to a compressed latent space. The decoder reconstructs the image ˆ I = D VAE ( x 0 ) .

Text-Vision Alignment Encoder (TIAE): This novel component, denoted E TE, is responsible for bridging the semantic gap between text and vision. It takes text embeddings E y (obtained from a frozen pretrained text model E Emb, e.g., CLIP [Radford et al., 2021]) as input and outputs a target prior mean µ T ( y ) = E TE ( E y ) ∈ R d . The TIAE is specifically trained to ensure that µ T ( y ) is both semantically meaningful (reflecting the content of y ) and aligned with the corresponding image latents x 0 in the V AE's latent space.

Mean-Conditioned OU Diffusion Process: Instead of a standard diffusion process mapping to N ( 0 , I ) , we employ an OU diffusion process [Zhou et al., 2023] to model the stochastic transition between the image latent x 0 and the text-conditioned prior distribution N ( µ T ( y ) , σ 2 T I ) . The process is parameterized by a score network s θ ( x t , t, y ) or, equivalently, a noise prediction network ϵ θ ( x t , t, y ) . The OU process inherently incorporates mean reversion towards µ T ( y ) , promoting stability and directed sampling.

3.3 TIAE Architecture and Training Objective

Architecture. To ensure seamless integration and potentially leverage existing performant architectures, we adopt a structure similar to modern diffusion models for the TIAE E TE, specifically using blocks inspired by DiT [Peebles and Xie, 2023] which handle conditional inputs effectively. It takes the text embedding E y as input and outputs the prior mean µ T ( y ) .

Training Objective. The TIAE is trained to produce priors µ T ( y ) that are: (a) aligned with corresponding image latents x 0 , and (b) preserve the semantic structure of the text embeddings E y . We use a composite loss:

(a) Latent Alignment Loss ( L align ): Encourages the TIAE output µ T ( y ) to be close to the V AE latent x 0 for corresponding image-text pairs ( I, y ) .

where x 0 = E VAE ( I ) and µ T ( y ) = E TE ( E y ) . This loss is visualized in Fig. 4.

(b) Semantic Consistency Loss ( L sem): Ensures that the distances between latent priors reflect the distances between text embeddings (using cosine similarity).

This encourages the structure of the text embedding space to be preserved in the prior space, as illustrated conceptually in Fig. 5.

TIAE

Text Embedding

Model

pretrained & frozen

Text

Text Embedding

Image

Encoder

Text

Latent

Image

Latent

Image

Decoder

Raw Image

Reconstruct Image

Alignment

Alignment

Generated Latent Distribution

TIAE

Mean

Text Embedding

Model

pretrained & frozen

Text

Text Embedding

All

Latent Distribution

Text1

Text2

Text3

Text4

TIAE

Text1 Embedding

Text Embedding

Model

pretrained & frozen

Text1

Text2 Embedding

Text2

Text3 Embedding

Text3

Text4 Embedding

Text4

...

...

Alignment

Embedding Similarity

Distribution Divergence

(a) Latent Representation

(b) Similarity Alignment

(c) Reconstruction Loss ( L rec ): To ensure µ T ( y ) can be decoded into meaningful images, we can add a reconstruction term comparing the decoded prior to the original image.

The total TIAE loss is L TIAE = w a L align + w s L sem + w r L rec, with weights w a , w s , w r .

3.4 Integration with OU Diffusion Process

Once the TIAE E TE is trained and frozen, we train the diffusion model component. LABridge employs an OU diffusion process defined by the forward stochastic differential equation (SDE):

where µ T ( y ) = E TE ( E Emb ( y )) is the target mean obtained from the frozen TIAE for a given text prompt y . This SDE defines a process that starts near x 0 at t = 0 and is drawn towards µ T ( y ) as t →∞ . The transition kernel q ( x t | x 0 , y ) corresponding to this SDE, assuming x t depends on x 0 and the target mean µ T ( y ) .

The goal is to learn the reverse process to sample x 0 ∼ q ( x 0 | y ) starting from the prior p ( x T | y ) = N ( x T ; µ T ( y ) , σ 2 T I ) . The reverse process is governed by the score function ∇ x t log q t ( x t | y ) . We follow the standard practice in diffusion models and train a neural network ϵ θ ( x t , t, y ) to predict the noise ϵ that generated x t from x 0 and µ T ( y ) via Eq. 8. The noise prediction objective for the OU process is:

where w ′ ( t ) is a time-dependent weighting function. The reverse probability flow ODE used for sampling is:

3.5 Training and Inference Algorithms

The overall training procedure for LABridge consists of two sequential stages (details of training procedure are provided in Algo. 1 in Appendix B. ):

Stage 1: TIAE Training. Train the Text-Vision Alignment Encoder E TE using the composite loss L TIAE on paired image-text data, with the V AE and text embedder frozen.

Stage 2: OU Process Training. Freeze the trained E TE. Train the noise prediction network ϵ θ for the OU process using the denoising objective L Bridge (Eq. 13).

The overall inference procedure for LABridge mainly based on Eq. 14 (details of inference procedure are provided in Algo. 2 in Appendix B.).

4 Theoretical Guarantees

We provide theoretical justification for the advantages of LABridge, highlighting improvements in text-vision alignment, sampling efficiency and stability standard diffusion models using fixed priors. Detailed statements and proofs are provided in Sec. D and E.

Theorem 4.1 (Enhanced Text-Vision Alignment) . The TIAE training objective, particularly L align and L sem, explicitly optimizes the prior mean µ T ( y ) to be (a) close to the corresponding image latent mean E [ x 0 | y ] and (b) preserve the semantic structure of the text embeddings E y . The OU process formulation reinforces this alignment during diffusion training and generation. (Ref: Thm. D.3, Prop. D.4 in the appendix).

Theorem 4.2 (Sampling Acceleration via Informed Initialization and Dynamics) . LABridge accelerates sampling due to two factors:

(i) Reduced Initial Error: Starting the reverse process from x T ∼ N ( µ T ( y ) , σ 2 T I ) provides an initial state closer (in expectation) to the target conditional mean E [ x 0 | y ] compared to starting from N ( 0 , σ 2 T I ) . (Ref: Thm. D.5 in appendix).

(ii) Directed Drift: The OU reverse dynamics (Eq. 14) include an explicit mean reversion term θ ( µ T ( y ) -x t ) which provides additional drift towards the text-aligned prior mean µ T ( y ) , supplementing the learned score/noise term and offering stronger guidance than standard diffusion drifts . (Ref: Thm. D.6 in appendix).

Theorem 4.3 (Improved Sampling Stability) . The inherent mean-reverting property of the OU process drift term θ ( µ T ( y ) -x t ) enhances the stability of the reverse sampling process, making it less prone to divergence compared to processes with zero or origin-centric drift, especially when score estimates may be imperfect. (Ref: Thm. D.8 in appendix).

Theorem 4.4 (Tighter Evidence Lower Bound) . Using the text-conditioned prior p ( x T | y ) = N ( µ T ( y ) , σ 2 T I ) results in a smaller expected KL divergence between the forward process endpoint distribution q ( x T | x 0 , y ) and the prior, compared to using a fixed prior p ( x T ) = N ( 0 , σ 2 T I ) , provided E [ x 0 | y ] varies significantly with y and µ T ( y ) approximates it well. This leads to a tighter ELBO. (Ref: Thm. D.9).

5 Experiment

We conducted a series of experiments to verify the effectiveness of LABridge under various settings.

Experiment Setup All code was performed on 8 A100 GPUs machine. For the first part, we employ DiT-XL/2 model to learn from scratch on benchmark dataset. We adopt NV-Embed-v2 1 as pretrained

text embedding model, which output 4096 ( 1 × 64 × 64 ) vector. The training utilized AdamW optimizer with a learning rate of 1 e -5 , β 1 = β 2 = 0 . 9 , weight decay of 0 . 03 , batch size of 16, and run for 200 epochs. All images are preprocessed with center crop and resized ( 1024 × 1024 ). We tune the weighting w a , w s and w r in the range of [0 , 1] . After a brief sweep, we used w a = 1 . 0 , w s = 0 . 5 , and w r = 0 . 2 .

Dataset. The training data comprises a selection from COYO [Byeon et al., 2022] datasets, following selection criteria in [Lin et al., 2024, Ren et al., 2024]. We evaluated performance on standard benchmarks: COCO [Lin et al., 2014], ImageNet [Deng et al., 2009], MJHQ-30K [Li et al., 2024a].

Evaluation Metrics. We evaluate all models on the COCO validation set [Lin et al., 2014], using two primary metrics: FID [Heusel et al., 2017] and CLIP score [Radford et al., 2021, Hessel et al., 2021]. Specifically, we report FID-10K, where prompts are randomly sampled from the validation set. The generated images for these prompts are then compared to reference images from validation set. In addition to these, we evaluate our models on the GenEval [Ghosh et al., 2024] and DPG-Bench [Hu et al., 2024] benchmarks, both of which are designed to measure text-image alignment. Finally, we report Inception Score [Salimans et al., 2016] and Precision/Recall [Kynkäänniemi et al., 2019] as secondary metrics to provide a more comprehensive evaluation.

Base Models. We implement our framework on five existing popular base models: stable-diffusionv1-5 (SD15) [Rombach et al., 2022], stable-diffusion-xl-v1.0-base (SDXL) [Rombach et al., 2022] with UNet architecture, and SD-3.5 [Esser et al., 2024] with MM-DiT architecture, PixArtα [Chen et al., 2023] with DiT architecture.

Learning from Scratch Experiments. We evaluated our method (three bridges) on the ImageNet 256×256 dataset with and without classifier-free guidance (CFG) and compared it with various stateof-the-art approaches (ImageNet 512x512 dataset results in Appendix C). The results are summarized in Tab. 2. Without CFG : Our method, CausalFusion-XL + LABridge, achieves the best performance among all evaluated models. Compared to the DiT-XL method, DiT-XL/2 + LABridge achieves significant performance improvements across all metrics. This demonstrates the effectiveness of our proposed LABridge in improving the quality and diversity of generated images. With CFG : When CFG is applied, our method again outperforms others, achieving the best FID and the highest IS, further highlighting its ability to generate high-quality and consistent text-conditioned images. These results validate that our approach is generalizable across different backbones and significantly enhances both image quality and text-image alignment.

Fine-Tuning Existing Pretrained Models. We conducted extensive experiments with various pretrained models with similar baseline Liu et al. [2024]. The experimental results, summarized in Tab. 2 and Fig. 6 (Appendix C.3), demonstrate that our method consistently outperforms baseline approaches across multiple evaluation metrics and datasets. Notably, our approach excels on the GenEval and DPG benchmarks, which are specifically designed to measure text-image consistency.

SD

X

L (50 N F E s )

bl o n d e  w o m a n  i n k a r a t

e  u n i for m

wi t

h r e d  t

o o r a n g e  g r

a d i e n t

cl e

SD X L  + Cr o s s

F

l o w

SD X L  + TI A E  (U N e

t )

SD

3.5-M

(50 N F E S)

SD 3.5-M +Cr os s F l ow

SD 3.5-M  + TI A E  (D

i T)

A  h i g h -fa s hi o n  cl

oa k  i n  s oft

wh i t

e ,

i t

s  e d g e s  cu r l

i n g  u pw a r

ds  a s  i

f

m o v e d  by  a n  u n s

e e n  fo r

ce .

A  cl o s e -u p o f fr

e s h  s t r

a w be r

r y  a n d

l e m o n  s

l i ce s  fl oa t

i n g  i n s pa r

kl i n g

wa t

e r , s

ur r o u n d e d  by  bu bbl

e s

a  r

e a l

i s t

i c ph o t

o o f a  g r

e e k  ce n a r

y

o f n i g h t

G r o u n d  Tr

ut h

The superior performance on these benchmarks underscores the strong competitiveness of our method in improving alignment between text and generated images. Moreover, to verify the generalizability of TIAE, we tested TIAE in combination with various other community text-to-image plugins. The results are shown in Fig. 7, demonstrating the robustness.

Ablation Studies. We conducted extensive ablation studies to evaluate the effectiveness of the different loss functions used in the LABridge training method, aiming to validate its correctness. The CLIP and FID metrics were tested on COCO-10k and MJHQ-30K, with the results summarized in Tab. 3. These results show that the training process combining all three loss functions consistently delivers the best performance across most backbones and benchmark datasets. In contrast, using a single loss

or a combination of two losses yields slightly inferior results. This highlights the effectiveness of our proposed LABridge approach.

6 Conclusion

We presented LABridge, a novel text-to-image generation framework designed to overcome semantic instability and slow sampling inherent in diffusion models. By employing a TIAE to create structured, text-conditioned prior aligned with image latents, and utilizing an OU diffusion bridge to connect these representations, LABridge establishes explicit text-vision consistency. LABridge offers a robust pathway towards efficient, high-quality conditional image synthesis.

References

Brian DO Anderson. Reverse-time diffusion equation models. Stochastic Processes and their Applications , 12(3):313-326, 1982.

Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu. All are worth words: A vit backbone for diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 22669-22679, 2023.

Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, and Adam et al. Letts. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 , 2023.

Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators, 2024. URL https://openai.com/research/ video-generation-models-as-world-simulators .

Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. Coyo-700m: Image-text pair dataset. https://github.com/kakaobrain/coyo-dataset , 2022.

Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426 , 2023.

Chaorui Deng, Deyao Zh, Kunchang Li, Shi Guan, and Haoqi Fan. Causal diffusion transformers for generative modeling. arXiv preprint arXiv:2412.12095 , 2024.

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A largescale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition , pages 248-255. IEEE, 2009.

Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems , 34:8780-8794, 2021.

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, and Frederic et al. Boesel. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first International Conference on Machine Learning , 2024.

Zach Evans, CJ Carr, Josiah Taylor, Scott H Hawley, and Jordi Pons. Fast timing-conditioned latent audio diffusion. arXiv preprint arXiv:2402.04825 , 2024.

Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems , 36, 2024.

Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffusion training via min-snr weighting strategy. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 7441-7451, 2023.

Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A referencefree evaluation metric for image captioning. arXiv preprint arXiv:2104.08718 , 2021.

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems , volume 30, pages 6626-6637, 2017.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems , volume 33, pages 6840-6851, 2020.

Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, and David J et al. Fleet. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303 , 2022.

Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu Ella. Equip diffusion models with llm for enhanced semantic alignment. arXiv preprint arXiv:2403.05135 , 2024.

Aapo Hyvärinen and Peter Dayan. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research , 6(4), 2005.

S Kang et al. Distilling diffusion models into conditional gans. ECCV , 2024.

Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Improved precision and recall metric for assessing generative models. Advances in neural information processing systems , 32, 2019.

Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2. 5: Three insights towards enhancing aesthetic quality in text-to-image generation. arXiv preprint arXiv:2402.17245 , 2024a.

Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. arXiv preprint arXiv:2406.11838 , 2024b.

Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. Diffusion-lm improves controllable text generation. Advances in Neural Information Processing Systems , 35: 4328-4343, 2022.

Shanchuan Lin, Anran Wang, and Xiao Yang. Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402.13929 , 2024.

Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, and Lu Jiang. Diffusion adversarial post-training for one-step video generation. arXiv preprint arXiv:2501.08316 , 2025.

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer VisionECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 , pages 740-755. Springer, 2014.

Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. Audioldm: Text-to-audio generation with latent diffusion models. arXiv preprint arXiv:2301.12503 , 2023.

Qihao Liu, Xi Yin, Alan Yuille, Andrew Brown, and Mannat Singh. Flowing from words to pixels: A framework for cross-modality evolution. arXiv preprint arXiv:2412.15213 , 2024.

Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. arXiv preprint arXiv:2401.08740 , 2024.

S Mei et al. Codi: Conditional diffusion distillation for higher-fidelity and faster image generation. CVPR , 2024.

T Nguyen et al. Swiftbrush: One-step text-to-image diffusion model with variational score distillation. CVPR , 2024.

William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4195-4205, 2023.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , pages 8748-8763. PMLR, 2021.

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical textconditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125 , 2022.

Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang, Jie Wu, Pan Xie, Xing Wang, and Xuefeng Xiao. Hyper-sd: Trajectory segmented consistency model for efficient image synthesis. arXiv preprint arXiv:2404.13686 , 2024.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10684-10695, 2022.

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems , 29, 2016.

Huiyang Shao, Qianqian Xu, Peisong Wen, Peifeng Gao, Zhiyong Yang, and Qingming Huang. Building bridge across the time: Disruption and restoration of murals in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 20259-20269, 2023.

Huiyang Shao, Xin Xia, Yuhong Yang, Yuxi Ren, Xing Wang, and Xuefeng Xiao. Rayflow: Instanceaware diffusion acceleration via adaptive flow trajectories. arXiv preprint arXiv:2503.07699 , 2025.

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning , pages 2256-2265. PMLR, 2015.

Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems , volume 32, pages 11895-11907, 2019.

Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations , 2021.

Jiayan Teng, Wendi Zheng, Ming Ding, Wenyi Hong, Jianqiao Wangni, Zhuoyi Yang, and Jie Tang. Relay diffusion: Unifying diffusion process across resolutions for image synthesis. arXiv preprint arXiv:2309.03350 , 2023.

Michael Tschannen, Cian Eastwood, and Fabian Mentzer. Givt: Generative infinite-vocabulary transformers. In European Conference on Computer Vision , pages 292-309. Springer, 2025.

Pascal Vincent. A connection between score matching and denoising autoencoders. Neural computation , 23(7):1661-1674, 2011.

Tong Wu, Zhihao Fan, Xiao Liu, Hai-Tao Zheng, Yeyun Gong, Jian Jiao, Juntao Li, Jian Guo, Nan Duan, Weizhu Chen, et al. Ar-diffusion: Auto-regressive diffusion model for text generation. Advances in Neural Information Processing Systems , 36:39957-39974, 2023.

Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou, Ying Nian Wu, Kevin Murphy, Tim Salimans, Ben Poole, and Ruiqi Gao. Em distillation for one-step diffusion models. arXiv preprint arXiv:2405.16852 , 2024.

Hongkai Zheng, Weili Nie, Arash Vahdat, and Anima Anandkumar. Fast training of diffusion models with masked transformers. arXiv preprint arXiv:2306.09305 , 2023.

Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon. Denoising diffusion bridge models. arXiv preprint arXiv:2309.16948 , 2023.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: The abstract and introduction accurately summarize the proposed LABridge framework, its components (TIAE, OU Bridge), the claimed benefits (improved alignment, accelerated sampling), and the intention to provide theoretical backing, which are reflected in the subsequent sections (Method, Theoretical Guarantees, Proof Details).

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We provide limitation in Sec. F.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: The paper includes dedicated sections for Theoretical Analysis (Sec.D) and Proof Details (Sec. E). Assumptions are stated (Assum. D.1), and detailed proofs are provided for the theorems and propositions presented (e.g., Thm. D.3, D.5, D.6, D.9, Propositions D.4, D.7, D.8).

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We describe the algorithms (Algo. 1, 2) and specific details (Sec. C) about the experimental setup, such as datasets used, specific hyperparameters (learning rates, batch sizes, parameters), training duration, evaluation metrics implementation, or baseline implementation details necessary to fully reproduce the claimed experimental results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: We will provide the source code and data after the draft is completed.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: Key details about the experimental setup are provided, including specific hyperparameter values (e.g., for TIAE loss weights w a , w s , w r , OU parameters θ, σ , learning rates, optimizer types, batch sizes), data splits for training/testing on the mentioned benchmarks, and specific schedules used.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: We conducted extensive experiments to demonstrate the effectiveness of our method.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We provide information about the computational resources used for the experiments.

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: This paper appears to be focused on algorithmic contributions and does not inherently conflict with the NeurIPS Code of Ethics.

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [No]

Justification: Our work primarily focuses on algorithm design and technical aspects, with limited direct societal impact. As such, the paper does not extensively discuss broader societal implications, positive or negative.

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: This question is not applicable.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: We cites prior work for components like pretrained VAEs (Stable Diffusion), text encoders (CLIP), and architectural inspiration (MMDiT).

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: Documentation requirements for new assets are not applicable.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The research presented in the paper does not involve crowdsourcing experiments or research with human subjects.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The research presented does not involve human subjects, so IRB approval is not applicable.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: Large language models (or derived text encoders like CLIP/T5) are used to obtain text embeddings for conditioning, which is standard practice in text-to-image generation. LLMs are not an important, original, or non-standard component of the core novel methodology presented in this paper.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces LABridge, a novel framework for text-to-image generation that enhances semantic alignment and sampling efficiency in diffusion models. Instead of relying on a fixed Gaussian prior, LABridge constructs text-conditioned latent priors using a Text-Image Alignment Encoder (TIAE) and employs an Ornstein–Uhlenbeck (OU) diffusion bridge to connect image latents with these priors. This structured alignment allows for more stable and directed denoising trajectories, improving both text-image consistency and generation speed. Theoretical analyses support the advantages of the OU bridge, and extensive experiments across multiple architectures and benchmarks demonstrate that LABridge consistently improves text-image alignment metrics while maintaining or surpassing image fidelity compared to state-of-the-art diffusion baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The work re-frames text-to-image synthesis as a stochastic bridge from image latents to a text-conditioned prior, realised with (i) a Text–Image Alignment Encoder that maps text into a latent prior and (ii) an OU diffusion bridge that converges to it.\n2. The paper introduces a mathematically sound method that replaces Gaussian priors with text-conditioned latents and applies an OU bridge, effectively improving alignment stability and sampling efficiency.\n3. Experiments on multiple backbones show consistent gains in alignment metrics (GenEval, DPG) and sampling efficiency, validating the method's effectiveness.\n\nWeaknesses:\n1. Limited Ablation on the OU Bridge:\nWhile the benefits of the OU bridge are well-motivated and theoretically justified, the paper lacks quantitative evidence that OU is indeed superior to other diffusion processes. For instance, in Table 1, the best performance on CausalFusion-XL is achieved using VP, not OU, and Table 2 only reports results with VP. Although VP and VE can be viewed as special cases of the OU process with specific hyperparameters, it remains unclear whether the general OU formulation consistently outperforms them.\n2. No Hyperparameter-Sensitivity Analysis for the OU Bridge:\nThe parameters that govern the OU dynamics (θ, σ) are fixed without any exploration of how different settings affect alignment quality, sampling speed, or stability. Without a sensitivity study, practitioners cannot gauge the robustness of the method or determine suitable defaults for new datasets and backbones.\n\nQUESTIONS:\nThe motivation is compelling, and the idea of using text-conditioned latents instead of a standard Gaussian prior is well-justified.\n\nHowever, as noted above, there remains some uncertainty regarding whether the OU bridge is truly necessary. It raises the concern that its inclusion may serve more as a novelty for novelty’s sake. If this concern is adequately addressed, I would be open to increasing my rating.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces LABridge, a new framework aiming to improve text-to-image generation. Diffusion models, while successful, face challenges like semantic instability and slow sampling primarily due to their dependence on fixed Gaussian priors. LABridge tackles these issues by integrating a Text-Image Alignment Encoder (TIAE) that develops structured text-conditioned priors to be aligned with image latents. It employs an Ornstein-Uhlenbeck (OU) diffusion bridge, allowing for smoother transitions between text-based priors and image latents, enhancing stability and lowering the denoising iterations required. The experimental evaluations reveal that LABridge delivers better text-image alignment and strong image quality compared to existing models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengthens：\n\n1) Introduction of LABridge framework, which improves the text-to-image generation process by enhancing text-image alignment and reducing sampling time through the use of a Text-Image Alignment Encoder (TIAE) and an Ornstein-Uhlenbeck (OU) diffusion bridge .\n\n2) The approach aligns structured text-conditioned priors directly with image latents, offering a more efficient and semantically consistent method for generating high-quality images.\n\n3) Experimental results show that LABridge achieves improved text-image alignment metrics and competitive image fidelity compared to state-of-the-art diffusion models .\n\n4) The framework also potentially accelerates sampling by providing informed initialization and dynamics \n\nWeakness & Question：\n\n1）Regarding the comparison with CLIP in terms of resource consumption for alignment training depending on the LABridge diffusion process, this work does not provide a detailed comparative analysis of resource consumption between LABridge and CLIP.\n\n2）Whether scaling up could bring greater benefits, the authors acknowledges the current improvements are modest and indicates that exploring the effects of scaling up is necessary to determine if greater benefits can be achieved.\n\nQUESTIONS:\nSEE WEAKNESS\n\nLIMITATIONS:\nSEE WEAKNESS", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors consider the problem of conditional generation using diffusion models. They notice that in practice, for all conditional inputs, the generation starts from the same standard Gaussian distributions. The authors argue that it leads to several problems, including instability, inefficient sampling, and weak text-vision alignment. The authors propose instead to learn a new model, which for every conditional input $y$ outputs the corresponding mean of the Gaussian used as a starting process $\\mu(y)$. Furthermore, the authors propose to learn the model in a way that minimizes the distance between the starting noise $\\epsilon \\sim \\mathcal{N}(\\mu(y), I)$ and ending data point, and to preserve the semantics arrangement between conditional encodings $\\mu(y)$. The authors evaluate the proposed approach by learning a model from scratch for class-conditional Image-Net generation and by finetuning an existing text-to-image model. The authors also provide an ablation study on the components of their approach.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The paper is well written. The proposed method is well described, supported by numerous high-quality illustrations.\n\n- The authors show in practice that their approach allows to get better quality of generation for both training from scratch and fine-tuning a wide set of existing text-to-image generation models.\n\n**Weaknesses:**\n- The experiment description has been completely moved to the Appendix. While I understand that it is due to the constraints on the length of the paper, I still think that it is better to briefly describe them in the main text. I think that it is okay to move some images to the Appendix, since there are plenty of them and the text is self-explanatory by itself.\n\n- Appendix C2 about ImageNet training details is empty.\n\n- It is said that the coefficients w_a, w_s, and w_r were tuned in the interval [0, 1]. However, there are no details about the final values used in practice.\n\n- I think there may be a misunderstanding about using the Ornstein-Uhlenbeck process and the Ornstein-Uhlenbeck Bridge for this process. \n\nIndeed, the Ornstein-Uhlenbeck process is described using the SDE:\n$$ dx_t = \\theta (\\mu - x_t)dt + \\sigma dW_t $$\nHere $\\mu$ and $\\theta$ are just the parameters of the process.\n\nEquation (8) provides the transition density function for a case when we start from the given $x_0$ and integrate the SDE.  The authors say that the OU bridge is a conditioned OU process that starts at x_0 and t=0 and ends at $x_t = y$ at $t = T$. However, for some reason, instead of deriving the OU bridge for this process, the authors just use the forward transitional density (Eq. 8) of the OU process. \n\nParticularly, for the OU bridge, the final distribution at $t=T$ is a delta function $\\delta\\_{y}(x)$, while for the OU process it is a Gaussian specified by (Eq. 8). Furthermore, the OU process for $\\mu_T=0$, corresponds to the usual VP SDE used in diffusion models. So, in fact, the authors use the regular diffusion framework with a “shifted” VP SDE process towards the mean provided by their additional model $\\mu_{T}(y)$. \n\nI think it would be better to omit the “bridge” part to avoid confusion with the branch of Diffusion Bridge Matching works. Also, it will further simplify what is already well-written and free up some space for experimental setup descriptions. Furthermore, since in fact the authors use a “shifted” VP SDE process, it should be possible to adapt not only PF-ODE sampling, but also other approaches like DDIM/DPM+ solvers (by introducing shift $\\mu(y)$ in the corresponding formulas), as well as only other approaches already developed for the diffusion models.\n\nFinally, since in fact the “shifted” VP SDE is used, there is no need to repeat several times about the “mean-reverting” property of the OU process as something unique only for the considered setup and not regular diffusion models.\n\n- Computational budget comparison to the standard diffusion and overhead introduced by the first stage of learning of an additional TIAE model.\n- The authors do not provide the code, which may cause reproducibility problems.\n- Statements labeled “Theorem 4.1–4.4” include phrases like “potentially accelerates” or “offers stronger guidance”, which are qualitative and lack formal conditions. Either supply rigorous statements and proofs or reclassify them as propositions or remarks. Note that Theorem 4.3 also holds for the standard VP SDE with $\\mu(y)=0$.\n- Theorem 4.3 is also applicable for the VP SDE, since it is also an OU process, with $\\mu(y) = 0$ for all conditions. Hence, mean-reversion is not unique to the proposed setup; any VP SDE drifts toward its mean. Repeating this point may obscure the real novelty, namely, conditioning the mean.\n\n**Overall assessment:**\nThe idea of condition-dependent noise initialization is clever and appealing, and the empirical results are convincing. However, the presentation would benefit from clearer terminology (drop “bridge” unless a true bridge is used), stricter formulation of theoretical claims, and fuller disclosure of experimental settings.\n\nQUESTIONS:\nYou use ODE sampling; however, DDIM and DPM++ can be adapted for your modified forward process. Did you try it?\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes LABridge, a new text-to-image diffusion generative framework. The framework is motivated by the observation that current T2I generation have three issues: (1) Different semantics using same diffusion priors, leading to ambiguity especially in early stage; (2) Sampling is slow in early diffusion process; (3) The text semantics are not aligned with image latents. To solve this issue, LABridge develops an Ornstein–Uhlenbeck (OU) process based diffusion framework. The OU process is a stochastic process that encourages the state variable to drift towards the equilibrium mean. Based on this property, the training objective of LABridge in general is to (1) let the text semantics become the equilibrium mean of the OU process in imagery latent space, and (2) training to ensure the model is able to predict the noise - similar to traditional diffusion loss. The first objective leads to the latent alignment loss, semantic consistency loss, and reconstruction loss, and the second objective leads to the bridge loss. Extensive experiment result indicates the proposed LABridge is effective under various scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The paper carefully identifies the potential issues of current diffusion T2I diffusion models: (1) Different semantics using same diffusion priors, leading to ambiguity especially in early stage; (2) Sampling is slow in early diffusion process; (3) The text semantics are not aligned with image latents. The motivation is clear.\n2. Introducing OU stochastic process seems reasonable since the stochastic process encourages the state variable to drift towards the equilibrium mean, which, when combined with the step of transforming the text embedding to imagery latents, can result in a process that generate based on the text semantics.\n3. Extensive experiments show that the LABridge lead to clear performance gain, and the ablation study shows the role of each design component.\n\n\nWeaknesses:\n1. The latent alignment loss design in (9): From my understanding, this objective tries to ensure the decoded text embedding is close enough to the encoded image without adding noise. I am wondering if this loss is easy to be optimized. My point is, if this loss becomes 0, if you decode the generated text embedding, would we directly obtain clean images in this case? In other words, this loss itself optimizes a text-to-image generator. If so, I think this loss is too complicated and hard to optimize.\n2. The semantic consistency loss design in (10): This objective tries to minimize the cosine-similarity difference between the image latent space and textual embedding space. This could also be hard to optimize, because the text embedding model is frozen and only the TIAE encoder is trainable. The ablation study in Table 3 seems also do not have a version where only this loss is not present.\n3. I am not sure I understand the OU process 100%, but from my understanding, the OU process is a stochastic process that encourages the state variable to drift towards the equilibrium mean, and it is intuitively reasonable to use here. I am just wondering if it is possible to compare this stochastic process choice with previous work such as [1] mentioned in the paper.\n\n[1] Denoising Diffusion Bridge Models\n\nQUESTIONS:\nOverall my questions mainly focus on the loss function designs. I might also miss why LABridge is not compared with DDBM mentioned in the paper. My questions are summarized as follow:\n\n1. A clearer explanation of loss function (9), since this itself would result in a T2I generative model;\n2. Similarly, I am wondering if it is easy to optimize loss function (10) because it involves align the meaning of cosine similarity in text embedding space with image latent embedding space\n3. A better comparison to DDBM, or please indicate why this is not possible\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}