LABridge: Text–Image Latent Alignment Framework via Mean-Conditioned OU Process
Authors: Huiyang Shao
Xin Xia
Yuxi Ren
XING WANG
Xuefeng Xiao
Venue: NeurIPS 2025 spotlight ()
Keywords: diffusion
alignment
OU Process
Primary area: applications

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
Huiyang Shao 1.2
Xin Xia
Yuxi Ren
Xing Wang"
Xuefeng Xiao
Tsinghua University
ByteDancce Seed
Abstract
Diffusion models have emerged as state-of-the-an image unthesis.Howweeer it often suffer from semantic instability and slow iterative denoising. We introduce Latent Alignment Framework (LiiBrigge), a novel Text-Image Latent Alignment Framework via an Ornstein-Uhlenbedd (OU) Process, which explicitly preserves and aligns textual and visual semantics in an aligned latent space. L.Bridgge employs a Text-Image Alignment Encoder (TIAE) encode text prompts into structured priors that are directly aligned with image latents. Instead of a homo- geneous Gaussian, Mean-Conditiones 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- -o-g benchmarks show that LABridge achieves better text- text-image alignment metric and competitive FID scores compared 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.
Introduction
Diffusion models [Sohl-Dickstein et al., 2015, Song and Ermon, 2019, Ho et al., 2020, Song et al., 2021] represent 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 al., 2022, Ramesh 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 ereation [Liu et al., 2023, Evans et al., 2024]. These models typically operate by defining forward diffusion process that gradually adds noise to data, transforming it into a simple prior distribution (often Gaussian), and then learning 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 time Markov chains, enabling powerful sampling and manipulation strategies like accelerated generation [Mei et al., 2024], timestep analysis, and model distillation [Shao al., 2025, Xie al., 2024, Nguyen al., 2024, Kang al., 2024].
Existing Challenges
Despite their success, standard diffusion models face limitations, particularly in text-to-image genera- tion, stemming largely from their reliance on a fixed, often unstructured prior:
1. Instability and Ambiguity from Homogeneous Priors: Conventional methods map the entire diverse data distribution (a) to single, fixed prior, typically ( I). This forces distinct semantic concepts (e.g., "cat," "dog," "landscape") onto the same simple latent structure. This collapsing priors can lead to instability, as the reverse process must disentangle these varied
"Corrsponding Author
Project Leader
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Cat Region
Cat Replon
Overlap
Prior
No Overlap
Hartthallon
Target Imsge
Dog Region
Prine
Comnsian
Prompi "A cur
Distr Duaribation
Terget luagge
Dog Region
Dagg Pries
Adgi
(a) Diffusion Sampling Process
(b) LABridge Sampling Process
Figure Comparision sampling process between traditional diffusion and LABridge
semantics from a homogeneous starting point. Furthermore, it can create ambiguity in score estimation (V æ. log p(æ+)), as paths originating from different initial data points might overlap significantly in the latent space near / = T, making the learned score an average that lacks precision for any specific semantic direction. We provide analysis in Appendix E.3
2. Inefficient Sampling: As shown in Fig. (a), the diffusion sampling process follows curved path. Without strong guidance, especially in early steps, the process can he slow, requiring many iterations (NFE Number of Function Evaluations) converge to a high-fidelity image that accurately reflects the conditioning. We provide analysis in Appendix D.3
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, framework designed to enhance text-vision alignment and accelerate sampling in diffusion models. L.ABridgee leverages two core ideas: dedicated encoder TIAE create structured, text-conditioned priors, and an OU diffusion process to connect image latents directly to these priors.
1. Text Encoder for Structured Priors:
employ a encoder to process text prompts (y) and generate corresponding latent representations μT (v). The latent (T) acts as structured, text- specific 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.
2. OU Diffusion Process for Alignment and Stability: We adopt OU process explicitly models the stochastic path between the image latent zo (obtained from VAE encoder) and distribution centered around the text-specific prior pr (3)),. i.c., ((pr(()) I). The OU process, known for its mean-reverting property, naturally pulls the state towards the target mean (r))), inherently promoting stability and alignment.
LABridge offers new perspective: it frames text-to-ine tont-to-image generation as learning stochastic process between the image latent manifold and manifold of text-conditioned priors. This explicit alignment via the process mechanism enhances semantic consistency and, by providing better starting point and direction, significantly speeds up the sampling process.
In summary, our contributions are threefold:
We propose LABridge, novel framework utilizing an encoder TIAE to generate structured text-conditioned priors μr (y) and an OU diffusion process to align them with image latents Øo.
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 text- image 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.

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2.1 Diffusion Processes and Score Matching
Diffusion Process. Let æ E R follow the data distribution (uma( A forward diffusion process defines a sequence of latent variables {2x hre f [0.7] starting from Zp Idata (x) and evolving towards simple prior distribution pr pr(rr) (0, I) as t goes from 0 to T' This evolution is often described by a stochastic differential equation (SDE) [Song et al., 2021]:
da:, = (# 1) dl + g(t) dwi;
where f(,t) is the drift function, g(t) is the diffusion coefficient, and w, is standard Wiener process. Generating new data involves reversing this process. The corresponding reverse-time SDE is given by [Anderson, 1982]:
dæ, [f g(t)² vx, log (a(a)) dt g(t) dw,,
(2)
where W2 is a Wiener process running backward in time, and pr (xx) is the marginal probability density of Ty. The crucial term is the score function Væ, po
Score Matching. In practice, the true score Va, log):( is unknown and is approximated by a time-dependent neural network 8 sp(xr, t), often conditioned on additional information y (like text embeddings), denoted (2 I, y). This network is trained by minimizing score matching objective [Hyvärinen and Dayan, 2005, Vincent, 2011]. For many diffusion processes (like VP and VE), the conditional score Va, log (r
xo) is tractable. A common training objective is:
(0) = _E- =0 ex.001) X(t) (3 + o,e, 8) - Va, a. log (e =0))| (3)
where (r(x 22)) NV (w ar of defines the transition kernel, and X(t) a weighting function. For this Gaussian kernel, Va, log (xt; at)) = -ax, 0,200/ = -//o.. This leads to the widely used noise prediction objective:
Cienvise (0) = mr X(()) ca(a + o,e,t,y) -
=l"].
(4)
where Eg is the network predicting the noise e, related to the score network by t, v) t,,
2.2 Diffusion Bridge Models
While standard diffusion maps data to a fixed prior, a diffusion bridge connects two specified endpoint distributions, (a:o) and pr(ær), which can both be complex. This is particularly relevant for tasks involving paired data (æo, IT) ~ Adata (an((Wo (Zo, ØT), such as image translation or, in our case, aligning image latents 26 with text-derived priors μr.
Stochastic Bridges via h- t-Transform. Given forward SDE Eq. (l), Doob's h-transform provides a way condition the process to start at an at f and end exactly at ær = y at T. The resulting bridge SDE is:
(5) where (r] xx) is the transition probability density of the original SDE from time to T. The term ha I. y. T) Vn, log Prg(y) æ.)) is the "guidance" term ensuring the endpoint constraint. For linear SDEs with Gaussian transitions (like VP, VE, OU), this term is often tractable.
dx; = [f( + g(t) Ve, Pri xx)] dt + gg() dw,.
Denoising Diffusion Bridge Models [hou et al., 2023]. Instead of exact endpoints, often want to sample from a conditional distribution g( | xr) given paired data (Iy, xr) ~ Quata This can be achieved by reversing bridge process designed such that its marginals q 2T) approximate qdata (Do, TT). Zhou et al. [2023] show that the reverse SDE for sampling a, given VT is:
dx. [f(a,,)) gt) (Va, log (x: py = y) Va, log log PT ( mx))] dt + g(t) dw.,
(6)
where V.c. (xx ØT = y) is the score of the conditional bridge distribution.
3

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2.3 Cean-Conditioned Ornstein-Uhlenbeck Process
Genstein-Uhlenble (OU) Process. The OU process a mean-reverting stochastic process often used model systems returning equilibrium. Its SDE is:
dx, 0 (1- az.) dl + dwy,
where is the rate mean reversion, fe is the equilibrium mean, and or is the volatility, The drift term 0( xx,) pulls the state æ, towards μ..
Orustein-Uhlenbeck Bridge (OUB). An OU process a conditioned OU process that starts at To at 0 and ends at æy y at t = '.. Its SDE can be derived using Doob's h-traasformm For the standard OU process with σ = starting from Zg at = O, the transition density to time T is Gaussian: (FT 2o) = NV (ar;; pa (æto u a²
2# (1 e- I)). The OUB derived from
this inherits the meennreevrtiing property ensures the endpoint constraint. our work, we use specific form of OU process connecting
distribution around (u) defined by the transition:
o² (1 2-20111
g( æo: HT) = =N (5 [1i #r +MT(1 -e -E-
20
This corresponds to an OU process SDE dæ, 0 22 dl + adw; This structure is key to LABridge. The parameter 0 controls the strength of reversion towards the text-conditioned prior mean pr. OUB processes can encompass VP and VE under specifie parameter choices.
3 Proposed Method: LABridge
3.1 Motivation: Structured Priors and Directed Diffusion
primary challenge lies in directly using text latent representations. Text data fundamentally different from typical latent variables. possible sentences and meanings is vast and does not easily map 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.
inherently infinite and unstructured; the space of
Instead, we must first represent text in a suitable format, typically through powerful pretrained embedding models (like CLIP. T5) that capture semantic meaning in nigh-dimensional vectors (Ey). However, simply using these embeddings as conditioning signals for standard diffusion process (mapping image to noise e) still relies the model implicitly learning the complex relationship between the text embedding space and the image manifold during the denoising process initiated from generic prior.
This motivates the need to represent text in continuous latent space that is explicitly aligned with the latent space of images. Rather than mapping images to generic noise, we pro- pose mapping images to text-conditioned pri- ors pr(y) that live in the similar latent space. This requires:
Text Latent
Image Latent
Cat
Diffusion
1. A mechanism to map text embeddings Ey to target latent priors pr (v).
Dog
2. Ensuring these priors per (y) are semantically consistent (similar texts map to nearby priors) and aligned with corresponding image latents.
Continuous text prior latent distribution
Corresponding image latent distribution
3. A generative process that efficiently connects these aligned endpoint distributions.
Figure 2: Conceptual illustration: Continuity of Tmxt- Latent Spaces
Alignment and

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Raw Imaas
Image Latent
Text Laicet
Test Eubealding
Image
TEAR
Suppopun
Made patsona frorn
Figure 3: Overview of the LABridge. An image / is encoded to Ty by &VAB- The corresponding text y embedded Ey by Exmb and then mapped a target prior mean pr(y) by the Text- Vision Alignment Encoder ET (TIAE). An OU diffusion process learns the stochastic path between 2p and the distribution N (g) o) During inference, sampling starts from IT and follows the learned reverse process dynamics towards 2o, which is then decoded to an image by D
3.2 LABridge Framework Overview
As depicted in Fig. 3, LABridge integrates three key components:
1. Pretrained Image Autoencoder: We utilize frozen Variational Autoencoder (VAE) with encoder
EVAR and decoder DVAR. The encoder maps input image / to latent representation æn E/AR(/) in Rd effectively moving the diffusion process to a compressed latent space. The decoder reconstructs the image = DAAE (20).
2. Text-Vision Alignment Encoder (TIAE): This novel component, denoted ΣTE, responsible for bridging the semantic gap between text and vision. It takes text embeddings E, (obtained from frozen pretrained text model Emb.. c.g., CLIP [Radford al., 2021]) input and outputs target prior mean (r(z) = ETE( E.y) E R The TIAE is specifically trained to ensure that μT(()) is both semantically meaningful (reflecting the content of y) and aligned with the corresponding image latents in the VAE's latent space.
3. Tran-Conditionee OU Diffusion Process: Instead of standard diffusion process mapping to (O, I). we employ an OU diffusion process [Zhou et al., 2023] to model the stochastic transition between the image latent wo and the text-conditioned prior distribution A (μr(y), of The process is parameterized by score network 89 (3 é, y) or, equivalently, a noise prediction network co y). The OU process inherently incorporates mean reversion towards (r promoting stability and directed sampling.
3.3 TIAE Architecture and Training Objective
Architecture. To ensure seamless integration and potentially leverage existing performant architec- tures, we adopt a structure similar to modern diffusion models for the TIAE &Tg, specifically using blocks inspired by DiT [Peebles and Xie, 2023] which handle conditional inputs effectively. It takes the text embedding Ey as input and outputs the prior mean (y).
Training Objective. The TIAE is trained to produce priors pr( that are: (a) aligned with corresponding image latents vp, and (b) preserve the semantic structure of the text embeddings Eyy- We use a composite loss:
(a) Latent Alignment Loss (Calign): Encourages the TIAE output (y) to be close to the VAE latent To for corresponding image-text pairs (1,
Lalign = E E w)00o (1))))) 2o
where 2tp ETAE(T) and (T( ETEl ( This loss is visualized in Fig. 4.
(b) Semantic Consistency Loss (Csem): Ensures that the distances between latent priors reflect the distances between text embeddings (using cosine similarity).
Seem
Eyg.35
(simeos( (HT (3:), (T(())) simeos (Eyg Eny))
(10)
This encourages the structure of the text embedding space to preserved the prior space, as illustrated conceptually in Fig.
5

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Algreent
Recentient Iooge /
Ran Image
Tex: LAIeE
Text
Nor Entesaty
Taage Encader
peorand Doocs
Figure 4: An overview of the TIAE training process. The input text is embedded using a pretrained model and then processed by TIAE to produce latent representation μT. The alignment loss encourages this fr to be close to the image latent 2g obtained from the corresponding raw image via the Image Encoder. An Image Decoder can reconstruct from prr (for free) or from Zo.
Generated Lateat Datribution
KK(Ph.PP)
Text
Text Eanheddlog
Mean fr
Duribaatoo Diverguce
Text Emshedding
Model presained
TEAL
Stdd dee
Byn
Byz
Eys
Textl
Embedding
Embooding Stealentty
En
Text Fasbedaling
Teat3
Br
Model
Tout
prevaised Snocs
Text3
Rembedsing
Teas4
Textd Embedding
Latent Representation
Datribution
(b) Similarity Alignment
Figure 5: Illustration of the latent space alignment and distribution analysis for TIAE. Text em- beddings (left) are encoded into latent distributions centered μr (center). The semantic con- sistency loss Cyem aims to ensure that similar text inputs (e.g., Textl, Text2) produce close latent means/distributions. preserving semantic proximity, shown by the embedding similarity matrix (right) compared to the latent distribution similarity.
(c) Reconstruction Loss ): To ensure μT(()) can be decoded into meaningful images, we can add a reconstruction term comparing the decoded prior to the original image.
Lree
(( wam (IDDAE((TT(OU) - -
(11)
The total TIAE loss is LHAE = W. Calign + WyCsee + Crees with weights Way Way Wr-
3.4 Integration with OU Diffussin Process
Once the TIAE ETE is trained and frozen, we train the diffusion model component. employs an OU diffusion process defined by the forward stochastie differential equation (SDE):
dæ: ((ur(v)) 2z adw;: e [0,
(12)
where pr = (EE(EEh is the target mean obtained from the frozen TIAE for given text prompt y. This SDE defines process that starts near 2e l 0 and is drawn towards pr(y)) as t + co. The transition kernel g<<)|<o. y) corresponding to this SDE, assuming depends on and the target mean (t))
The goal is to learn the reverse process to sample 2g ~ g(xoly) starting from the prior p( p(zrly) = M(Er; (v)) I). The reverse process is governed by the score function Vz, log ge (v/.. We follow the standard practice in diffusion models and train a neural network cœ t, y) to predict the noise e that generated æ; from Dn and (T( via Eq. 8. The noise prediction objective for the OU process is:
BBddge E2-24(0,T) e ((0.)) w (4((() (160(ar, 4, <112]
(13)

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where w (t) is a time-dependent weighting function. The reverse probability flow ODE used for sampling
a²
dæ (0(pr()) xx) +
20, c,(2,,t,))
(14)
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. in Appendix B.
Stage 1: TIAE Training. Train the Text-Vision Alignment Encoder &TE using the composite loss
LTAA on paired image-text data, with the VAE and text embedder frozen.
Stage 2: OU Process Training. Freeze the trained ΣTЕ. Train the noise prediction network eø for the OU process using the denoising objective LBridge (Eq. 13),
The overall inference procedure for LABridge mainly based on Eq. 14 (details of inference procedure 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 Lulign and Crem, explicitly optimizes the prior mean pr(y) be (a) close to the corresponding image latent mean E[zolvl and (b) preserve the semantic structure of the text embeddings Ey. The OU process formulation reinforces this alignment during diffusion training and generation. (Ref: Thm. D.3, Prop. 4 in the appendix).
Theorem 4.2 (Sampling Acceleration via Informed Initialization and Dynamics). LABridge acceler- ates sampling due to two factors:
(i) Reduced Initial Error: Starting the reverse process from ør N(urry)) If provides an initial state closer (in expectation) to the target conditional mean E[ compared to starting from (0, T). (Ref: Thm. D.5 in appendix).
ii) Directed Drift: The OU reverse dynamics (Eq. 14) include an explicis mean reversion term ((ur(y) - 2..) which provides additional drift towards the text-aligned prior mean pr((),, sup- plementing the learned scorehoise term and offering stronger guidance than standard diffusion drifts. (Ref: Thm. D.6 in appendix).
Theorem 4.3 (Improved Sampling Stability). The inherent reverrevn property of the OU process drift term (prr()) xx)) enhances the stability of the reverse sampling process, making it less prone to divergence compared 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(ærly) = N (y), of results in a smaller expected KL divergence between the forward process end- point distribution q(<r) v) and the prior, compared to using a fixed prior p(ær) = NO, I),, provided E ( ly] varies significantly with y and (y((y) upproximates well. This leads a lighter 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, employ DIT-XL/2 model to learn from scratch on benchmark dataset. We adopt WW-Embed-v2 as pretrained
hanps//touggiggfiee.ooo/vviaaaa E2b
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text embedding model, which output 4096 (1 x 64 64) vector. The training utilized Adami optimizer
with a learning rate of 1e 5, 31 32 = 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 x 1024). We tune the weighting Was Ws and w, in the range of [0, 1.. After brief sweep, we used wa 1.0, w's = 0.5, and uc, = 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 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 metries: 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 al., 2024] benchmarks, both of which are designed to measure text-image alignment. Finally, we report Inception Score [Salimans et al., 2016] and Precision/Recal [Kynkäänniemí et al., 2019] as secondary metrics to provide a more comprehensive evaluation.
Base Models. We implement our framework on five existing popular base models: v1-5 (SD15) [Rombach et al., 2022], mable-diffusion-te a1-vv.0-basse (SDXL) [Rombach et al., 2022] with UNet architecture, and SD-3.5 [Esser et al., 2024] with MM-DiT architecture, PixArt-a [Chen al., 2023] with DiT architecture.
stable-diffusion-
w/o CFG
CFG
Model
FID
18 f
Pre.
Rec.
FID
I8 f
Pre.
Rec.
GIVT [Tschannen et al,, 2025]
5.67
0.75
0.59
3.35
0.84
0.53
MAR-B [Li al., 2024b]
3.48
192.4
0.78
0.58
2.31
281.7
0.82
0.57
LDM-4 [Rombach al., 2022]
10,56
103.5
0.71
0.62
3.60
247.7
0.87
0.48
CausalFusion-L. [Deng et al., 2024]
10.56
103.5
0.71
0.62
1.94
264.4
0.82
0.59
ADM [Dhariwal and Nichol, 2021]
10.94
0.69
0.63
4.59
186.7
0.82
0.52
DiT-XL [Peebles and Xie, 2023]
9.62
121.5
0.67
0.67
2.27
278.2
0.83
0.57
SiT-XT. [Ma al., 2024]
8.3
2.06
270.3
0.82
0.59
ViT-XL [Hang ct al., 2023]
8.10
2.06
U-ViT-H/2 [Bao ct al., 2023]
6.58
2.29
263.9
0.82
0.57
MaskDiT [Zheng et al., 2023]
5.69
178.0
0.74
0.60
2.28
276.6
0.80
0.61
RDM [Teng al., 2023]
5.27
153.4
0.75
0.62
1.99
260.4
0.81
0.58
CausalFusion-XL, [Deng et al., 2024]
3.61
180.9
0.75
0.66
1.77
282.3
0.82
0.61
DIT-XL/2 + LABridge (VE)
5.74
152.1
0.70
0.64
2.13
279.9
0.84
0.57
DIT-XI./ + I.A Bridge (VP)
4.95
164.4
0.76
0.67
1.95
285.4
0.86
0.60
DiT-XL/2 + LABridge (OU)
3.83
179.2
0.76
0.65
1.84
289.3
0.87
0.62
CausalFusion-Xi LABridge (VP)
3.49
182.4
0.77
0.68
1.69
291.3
0.86
0.63
Table I: Benchmarking class-conditional image generation on ImageNet 256 x 256. The left half
is without CFG, the right half is with CFG. Best results are in bold, second best are underlined.
Learning from Scratch Experiments. We evaluated our method (three bridges) on the ImageNet 256x256 dataset with and without classifier-free guidance (CFG) and compared it with various state- of-the-art approaches (ImageNet 5 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 applicd, 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.
8

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COCO-10K
MJHQ-30K
Text-A Text-Alignment
Method
FID
CLIP 1
FID
CLIP f
GenEval f
DPG
Stable Diffusion VI.5
Comparision
SDI5-Bas Base [Rombach et al., 2022]
+5.81+.04
28.03+.08
+3.54+.0
28. +.0
+.48+
70.6441.0
SD15 + CF Liu et al. [2024]
14
28.52
12.62
28. 90.002 03
0.54 04
71.35=
SD15 L.Brrdgge (VP, UNet)
13.82
29.01
11.63
29.42:..2
0.57
72.42::.02
Stable Diffusion XI. Comparision
3DXI.-Base [Rombach ct al., 2022]
11.68:.04
28.83 04
10.55
29.63# o1
0.56 03
75.52:t.0
SDXL CF (Liu et al. [2024]
12.69
29.33:
9.59
30.15: 03
0.62
76.53::.0
SDXL + LABridge(VEE VP, UNet)
12.72:
29.82:
8.55
30.63:
0.65
77.21::
Stable Diffusion 3.5 Medium DiT Comparision
SD3.5-M [Esser et al., 2024]
9. m
29.91
8.45
30.71
0.62
83.31
SD3.5-M CF Liu et al. [2024]
8.89
30.43+.0z
7.28+
31.23
0.65
8:
SD3.5-M LABridge( VP, DiT)
7
30.92.:01
7.44
31.72:100 02
0.67
85.31
Stable Diffusion
3.5 Large DiT Comparision
SD3.5-L [Esser al., 2024]
30
5.84+
31.41
0.66+
84.52: 02
SD3.5-L CF Liu et al. [2024]
6.33
31.36::.0
4.84
31.89 02
0.68
85.52:1.00
SD3.5-L + LABridge(VP,, DiT)
5.
31.87
3.821.02
32.39
0.69
85.28.1.01
PixArt DiT Compartsion
PixArt-a [Chen al., 2023]
11.24 1.02
29.52:
9.65
30.01 04
0.49
75.42:1.00
PixArt-a CF Liu et al. [2024]
10.231.03
30.02: o1
8. 8.62..03
30,53..00 00
0.51
76.65:1.01
PixArtea LABridge (VP, DIT)
9.23
30.51
7.63
31.01
0.54
77.76.103
Table Quantitative comparison of state-of-the-ee models across various architectures and bench- marks for different metrics.
SOXE
SPXXE
SD3.5-M (50 NEES)
SD3.5-M CreriPiow
S03 TIAE
Ground Track
Figure 6: Qualitative comparison of LABridge incroprate pretrained models against raw models. Please zoom to check details, lighting, and aesthetic performances. All methods that do not have an NFE in place default to 50.
The superior performance 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. 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 single loss

---

Module
COCO-10K
MJHHQ-30K
Lise
Liem
Lalign
CLIP
FID 4
GenEval
DPG
CLIP
FTD
GenEval 1
DPG
Stable Diffusion VI.5 Ablation
29.01
13.82
0.57
72.42
29.42
11.63
0.57
72.42
/
28.90
14.01
0.55 =
71.85
29.25
11.90
0.55 +
71.54
28.56 = 03
14.32 +
0.54 = 84
71.35 m
28.97
+ 02
12.04 04
0.54 1
71.35 + 04
28.12 04
14.85 +
0.53 =
70.65 06
28.45
+ 03
12.38 06
0.53 1 04
70.65 + 45
27.89 as 15.12 on
0.52
69,80
27.83
+
12.67
0.52 +
69.80
27.45
15.54
0.51
69.32
27.32
13.01
0.51
69.32
Stable Diffusion XI. Ablation
29.82 .ss 12.82
0,65
77.21
30,63
8.55
0.65
77.21
29.61
13.09
0.63
76.75
30.65
8.85
0.63
76.70
29.45 04
12.79
0.62 = 76,53
30.12
8.98 =
0.62 1
76.53
29.12 = 05
13.42
+:
0.60 = 06 75.90 2 08
29.78 + 06
9.27 = 04
0.60 1 08
75.90 1 04
28.78 06 13.83
0:05
0.58 = . 75.30
#.0 29.32
+ 06
9.64
0.58 + 06 75.30 + 05
28.45
14.21
0.57
74.74
#.07
28.95
+
10.02
0.57
74.74
Table 3: A Evaluation of Performance Following the Ablation Studies of Lrees Ciems and Catign
Modules in Stable Diffusion V1.5 and Stable Diffusion XL Models across COCO-10K and MJHQ- 30K Datasets.
or combination of losses yields slightly inferior results. This highlights the effectiveness of our proposed LABridge approach.
Conclusion
We presented LABridge, novel text-to-image generation framework designed to overcome semantic instability and slow sampling inherent in diffusion models. By employing a TIAEE to create structured, text-conditioned prior aligned with image latents, and utilizing 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.
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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]
The abstract and introduction accurately summarize the proposed LABridge frame- work, 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).
Justification:
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 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).
Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main experi- mental 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 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 data and code
Question: Does the paper provide open access to the data and code, with sufficient instructions 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.
Experimental setting/details
Question: Does the paper specify all the training and details (c.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 hyperparam- eter values (c.g., for TIAE loss weights Wa W., wy, OU parameters 0, 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 method.
8. Experiments compute resources
14

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For each experiment, does the paper provide sufficient information on the computer resources (type compute workers, memory, time of execution) needed reproduce the experi- ments?
Question:
Answer:
[Yes]
Justification: We provide information about the computational resources used for the experiments.
9. Code of ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurIPS Code of Ethics https: / neurips..
Answer: [Yes]
contributions and does not inherently Justification: This paper appears to be focused on algoritimiccontriiutiionn 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 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 high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [NA]
Justification: This question 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 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 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]
15

---

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 the core methods in this research? Note that if the LLM is used only for writing, editing, 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) used to obtain text embeddings for conditioning, which is standard practice in text-to-image generation. LLMs important, original, non-standard component of the core novel methodology presented in this paper.
16


Reviews:
[{"review_id": "UtlpyPOPIi", "reviewer": "Reviewer_xMBY", "summary": "This 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.", "questions": "Overall 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", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength:\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", "was_revised": true, "final_justification": "The rebuttal has clarified my concerns regarding the loss deigns choices, and also clarified my understanding of the relation between DDBM. I have no further concerns, so I change the score to \"Accept\".", "rebuttal": "We sincerely thank the reviewer for their valuable feedback and insightful questions.  We provide the following clarifications to address the questions raised.\n\n---\n\n### **1. On the Latent Alignment Loss ($L_{\\text{align}}$)**\n\n**Goal is Alignment:** The objective of this loss is to align the text latent space with the image latent space, rather than to minimize the loss as much as possible. Therefore, as long as the alignment is achieved, it is not necessary to optimize the loss to a very low value. Moreover, experiments show that this loss is not difficult to optimize; it decreases quickly and tends to stabilize.\n\n**Conditional Mean:**\nIf the text-to-image mapping were one-to-one, optimizing $L_{\\text{align}}$ to zero would indeed create a direct text-to-image generator. However, the relationship is one-to-many: a single prompt $y$ (e.g., \"a dog\") corresponds to a vast distribution of possible images, and thus a distribution of image latents $x\\_0$.\n\nThe loss is defined as an expectation over the data distribution:\n$$L\\_{\\text{align}} = \\mathbb{E}\\_{(x\\_0, y) \\sim q\\_{\\text{data}}} [||\\mu\\_T(y) - x\\_0||\\^2]$$\n\nFor any given text prompt $y$, the expression that minimizes this loss is the conditional mean of the image latents, i.e., $\\mu_T(y) = \\mathbb{E}[x_0 \\mid y]$. Therefore, our TIAE is trained to map a text prompt to the **average latent representation** (or \"center of mass\") of all corresponding images.\n\nDecoding this average latent $\\mu_T(y)$ directly would likely produce a generic or blurry image, not a specific, high-fidelity instance. The purpose of this loss is not to be a standalone generator, but to establish a semantically meaningful **anchor point** in the latent space for the diffusion process to start from. The OU diffusion bridge is then essential for sampling diverse, high-quality instances *around* this well-positioned mean, which is far more efficient and stable than starting from a generic Gaussian prior.\n\n---\n\n### **2. On the Semantic Consistency Loss ($L_{\\text{sem}}$)**\n\n**Powerful TIAE Architecture:**\nWhile the text encoder is frozen, the TIAE architecture (inspired by DiT) is a powerful Transformer-based model. It possesses significant expressive capability to learn a complex, non-linear mapping from the text embedding space to the image latent space. Our goal is not to perfectly replicate the geometry of the text space but to preserve its crucial **semantic structure**. For instance, if in the text space \"king\" is closer to \"queen\" than to \"apple,\" then the corresponding latent priors $\\mu_T$ should maintain a similar relative arrangement. The TIAE is flexible enough to learn this.\n\n**Crucial Role as a Regularizer:**\nThis loss serves as a vital regularizer. While $L_{\\text{align}}$ anchors the priors for prompts seen during training, $L_{\\text{sem}}$ ensures that the latent space **between these anchors** is structured in a semantically coherent manner. This is critical for generalizing to new or compositional prompts, as it encourages the learned prior manifold to be smooth and meaningful, preventing it from collapsing or becoming disorganized in unseen regions.\n\n**Ablation Study without $L_{\\text{sem}}$:**\nWe apologize for the omission in the original ablation table. We have conducted the requested experiment and agree it provides valuable insight. Below is an updated version of the ablation results from Table 3, including a new row (in bold) where only $L_{\\text{sem}}$ is disabled.\n\n**Table 3 (Updated): Ablation on Stable Diffusion XL (COCO-10K)**\n\n#### **Stable Diffusion V1.5 Ablation**\n\n| $L\\_{\\text{rec}}$ | $L\\_{\\text{sem}}$ | $L\\_{\\text{align}}$ | COCO-10K CLIP ↑ | COCO-10K FID ↓ | COCO-10K GenEval ↑ | COCO-10K DPG ↑ | MJHQ-30K CLIP ↑ | MJHQ-30K FID ↓ | MJHQ-30K GenEval ↑ | MJHQ-30K DPG ↑ |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| ✓ | ✓ | ✓ | **29.01 ±.02** | **13.82 ±.04** | **0.57 ±.02** | **72.42 ±.03** | **29.42 ±.01** | **11.63 ±.05** | **0.57 ±.01** | **72.42 ±.02** |\n| ✓ | | ✓ | 28.90 ±.03 | 14.01 ±.04 | 0.55 ±.03 | 71.85 ±.04 | 29.25 ±.02 | 11.90 ±.04 | 0.55 ±.03 | 71.54 ±.04 |\n\n\n#### **Stable Diffusion XL Ablation**\n\n| $L\\_{\\text{rec}}$ | $L\\_{\\text{sem}}$ | $L\\_{\\text{align}}$ | COCO-10K CLIP ↑ | COCO-10K FID ↓ | COCO-10K GenEval ↑ | COCO-10K DPG ↑ | MJHQ-30K CLIP ↑ | MJHQ-30K FID ↓ | MJHQ-30K GenEval ↑ | MJHQ-30K DPG ↑ |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| ✓ | ✓ | ✓ | **29.82 ±.03** | **12.82 ±.02** | **0.65 ±.03** | **77.21 ±.02** | 30.63 ±.03 | **8.55 ±.02** | **0.65 ±.02** | **77.21 ±.01** |\n| ✓ | | ✓| 29.61 ±.04 | 13.09 ±.03 | 0.63 ±.04 | 76.75 ±.03 | **30.65 ±.04** | 8.85 ±.03 | 0.63 ±.04| 76.70 ±.03 |\n\n\nThe results show that removing $L\\_{\\text{sem}}$ leads to a noticeable drop in performance, particularly on the text-alignment metrics (GenEval, DPG), confirming its importance for preserving semantic structure and improving generalization.\n\n### **3. Comparison to Denoising Diffusion Bridge Models (DDBM)**\n\nOur process does not model a \"bridge\" in the strictest mathematical sense of conditioning on a specific endpoint $x_T = y$ (a delta function). Instead, it connects an initial point $x_0$ to a target *distribution* $\\mathcal{N}(\\mu_T(y), \\sigma_T^2 I)$.\n\nOur choice of the term \"bridge\" was motivated by its broader use in recent literature, such as in **Denoising Diffusion Bridge Models (DDPMs) [Zhou et al., 2023]**, which also focuses on connecting two endpoint distributions rather than two fixed points. Our work frames text-to-image generation as learning a stochastic bridge between the image latent manifold and a manifold of text-conditioned priors.\n\nOur key innovations lie in **how we define the two endpoint distributions for the text-to-image task**:\n1.  **The \"Start\" Distribution ($p_0$):** We operate in the latent space of a pre-trained VAE, so our starting points $x_0$ are drawn from the manifold of encoded real images.\n2.  **The \"End\" Distribution ($p_T$):** This is our main contribution. Instead of a fixed prior like $\\mathcal{N}(0, I)$, we introduce the **Text-Image Alignment Encoder (TIAE)** to create a *structured, text-conditioned prior distribution* for the endpoint: $p(x_T \\mid y) = \\mathcal{N}(\\mu_T(y), \\sigma_T^2 I)$.\n\nTherefore, DDBM is the foundational tool we build upon. We use its principles to construct a bridge, but our novelty is in creating and aligning the text-conditioned target endpoint $\\mu_T(y)$ that makes the bridge semantically direct and efficient. The Ornstein-Uhlenbeck (OU) process is a specific type of bridge we choose for its mean-reverting property, which perfectly complements our goal of steering the generation process towards the text-aligned prior $\\mu_T(y)$.\n\nWe hope these clarifications have adequately addressed your concerns. We are grateful for your time and constructive feedback, which have helped us improve the clarity of our paper."}, {"review_id": "tIUCE0XVL8", "reviewer": "Reviewer_RuZM", "summary": "The 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.", "questions": "You use ODE sampling; however, DDIM and DPM++ can be adapted for your modified forward process. Did you try it?", "limitations": "No", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "During the rebuttal, the authors fully addressed my concerns.", "rebuttal": "We sincerely thank the reviewer for the thorough and constructive review. Your insightful comments and suggestions are invaluable for improving the quality and clarity of our work. Below, we address each point in detail.\n\n---\n\n### **1. Terminology: \"Ornstein-Uhlenbeck (OU) Bridge\"**\n\nWe agree that our process does not model a classical \"bridge\" conditioned on a fixed endpoint $x_T = y$ (a delta distribution). Instead, it connects an initial point $x_0$ to a target *distribution* $\\mathcal{N}(\\mu_T(y), \\sigma_T^2 I)$.\n\nOur use of \"bridge\" follows recent literature (e.g., **Denoising Diffusion Bridge Models [Zhou et al., 2023]**), which also connect distributions rather than fixed points. We frame text-to-image generation as learning a stochastic bridge between the image latent manifold and a text-conditioned prior manifold.\n\nWe appreciate your suggestion to view our method as a **\"shifted\" VP SDE**. Indeed, our OU process resembles a VP-like process with a learned, text-conditioned mean-reversion point $\\mu_T(y)$ rather than the origin, which is our key contribution.\n\nTo avoid confusion, we will revise Sections 2.2 and 3.2 to:\n- Explicitly clarify that the \"bridge\" connects an initial point to a target *distribution*, not a fixed endpoint.\n- Introduce the \"shifted VP-SDE\" interpretation as an intuitive understanding of our model, emphasizing that learning the semantic shift $\\mu_T(y)$ is novel.\n\n---\n\n### **2. Mean-Reverting Property**\n\nYou are correct that standard VP-SDEs are OU processes mean-reverting to the origin.\n\nOur novelty lies in conditioning the mean-reversion on a **semantically meaningful, non-zero mean $\\mu_T(y)$** dependent on the text prompt. Standard VP-SDEs revert to zero regardless of conditioning, forcing the score function to steer the trajectory entirely.\n\nOur explicit drift term $\\theta(\\mu_T(y) - x_t)$ provides stronger, direct guidance, improving stability and accelerating sampling—especially when the target $E[x_0|y]$ is far from the origin.\n\nWe will clarify in Sections 4.2 and 4.3 that the innovation is not the mean-reversion itself but the conditioning of the reversion mean, which enhances guidance compared to a fixed origin.\n\n---\n\n### **3. Theorems 4.1–4.4**\n\nWe will reclassify **Theorems 4.1–4.4** as **Propositions** and **Heuristic Arguments**, and refine the statements to specify precise conditions, removing vague terms like \"potentially accelerates.\"\n\n---\n\n### **4. Experimental Details (Section 5, Appendix C.2, Hyperparameters)**\n\nWe apologize for the omissions.\n\n- **Experiment description:** Due to page limits, many details were moved to the appendix. We will add a concise summary of the experimental setup in Section 5.\n- **Appendix C.2:**  C.2 Learning from Scratch (ImageNet 512x512) does not actually describe the experimental setup and details. Essentially, it presents the experimental results of Table 5. Due to LaTeX formatting/layout issues, the table is placed at the top of the page, making it appear as if Appendix C.2 is empty.\n- **Loss weights $w_a, w_s, w_r$:** After a brief sweep, we used $w_a=1.0$, $w_s=0.5$, and $w_r=0.2$. The alignment loss $L_{align}$ was prioritized, with semantic and reconstruction losses as regularizers.\n\nWe will incorporate these details in the final version and ensure appendices are complete.\n\n---\n\n### **5. Computational Budget and TIAE Overhead**\n\n- **TIAE training (Stage 1)** is significantly lighter than diffusion model training (Stage 2).\n- The TIAE model is smaller than the main U-Net/DiT backbone and converges quickly on a simple regression objective. Empirically, Stage 1 consumes about 15–20% of Stage 2’s computational budget. For example, fine-tuning SDXL took ~ 260 GPU-hours on A100s for TIAE training.\n- **Inference overhead:** Negligible, as TIAE requires only a single forward pass to compute $\\mu_T(y)$. Inference time per 1024×1024 image is about 0.3–0.6 seconds.\n\nWe will add a paragraph on computational cost in the revised experimental section.\n\n---\n\n### **6. Code Availability**\n\nAs noted in the checklist, we will release full source code, pretrained TIAE models, and training configs upon publication.\n\n---\n\n### Additional Question: ODE Sampling and Alternative Samplers\n\nWe conducted preliminary experiments adapting DPM-Solver++ to our LABridge framework. Results on ImageNet 256×256 (FID↓) are:\n\n| NFE | Base (DDIM) | Base (DPM++) | LABridge (PF-ODE) | LABridge (DPM++) |\n|:---:|:-----------:|:------------:|:-----------------:|:----------------:|\n| 10  | 18.5        | 16.2         | 14.8              | **13.9**         |\n| 20  | 9.85        | 8.10         | 7.93              | **6.74**         |\n| 50  | 4.15        | 3.80         | 3.20              | **3.09**         |\n\nThese indicate LABridge provides a strong foundation that advanced samplers can further improve.\n\n---\n\nThank you again for your valuable feedback."}, {"review_id": "EUzO25kKxg", "reviewer": "Reviewer_gxFX", "summary": "This 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.", "questions": "SEE WEAKNESS", "limitations": "SEE WEAKNESS", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengthens：\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.", "was_revised": true, "final_justification": "Accepted", "rebuttal": "We sincerely thank the reviewer for their valuable feedback and insightful questions.  Below, we provide a more comprehensive response with expanded data to address each point in detail.\n\n---\n\n### **1. Resource Consumption of LABridge Alignment vs. Standard CLIP Usage**\n\nThe core idea of LABridge is to invest a **one-time, offline training cost** for the Text-Image Alignment Encoder (TIAE) to gain significant, lasting improvements in semantic alignment, sample quality, and inference efficiency.\n\n**Detailed Cost-Benefit Analysis:**\n\nThe TIAE module is a lightweight transformer-based network, inspired by DiT blocks. Its objective is to learn a direct mapping from a text embedding space ($E\\_y$) to an image latent space ($\\mu\\_T(y)$). This is a simpler, more constrained task than training the entire diffusion U-Net, which must learn a complex denoising function across multiple timesteps.\n\n*   **Upfront Training Cost:** The TIAE is trained on a large-scale image-text dataset (e.g., a subset of COYO-700M as mentioned in our appendix). While this is not a trivial cost, it is an order of magnitude smaller than training the base diffusion model from scratch.\n*   **Amortized Benefits:** This one-time cost is amortized over the model's lifetime, yielding substantial returns:\n    1.  **Superior Alignment:** As shown in our main results (Table 2), LABridge consistently improves scores on alignment-focused benchmarks like GenEval and DPG, leading to images that better reflect the text prompt.\n    2.  **Accelerated Inference:** The structured prior and OU bridge dynamics provide a much better starting point and a more direct path for the reverse process. As demonstrated in Table 6, this reduces the required number of function evaluations (NFE) by **50% or more** for equivalent image quality, directly cutting inference time and cost.\n\nTo make this explicit, we will add a more detailed computational cost analysis to the appendix. The table below provides a realistic estimate based on our experience and publicly available data on training large-scale models.\n\n**Proposed Addition (Appendix C.5: Computational Cost Analysis):**\n\n**Table 1: Estimated Computational Cost Comparison (Based on DiT-XL Architecture)**\n\n| Stage | Standard Diffusion (e.g., DiT-XL) | LABridge (DiT-XL base) | Notes & Justification |\n| :--- | :--- | :--- | :--- |\n| **Foundation Model Pre-training** | ~2840 A100-hours | ~2840 A100-hours | Cost to train the main diffusion U-Net from scratch on a large-scale dataset like ImageNet-21K. This is a one-time cost for the foundation model. |\n| **TIAE Alignment Pre-training** | N/A | **~260 A100-hours** | A one-time cost for training the TIAE module on a dataset like COYO-700M. This represents **<10%** of the foundation model's training compute. |\n| **Total Upfront Cost** | ~2840 A100-hours | ~3100 A100-hours | A modest increase in total pre-training compute for a significant downstream benefit. |\n| **Community Fine-tuning** | Baseline Cost | ~Baseline Cost | When fine-tuning on a new dataset, the TIAE is frozen. The fine-tuning cost for the U-Net is therefore comparable to the baseline. |\n| **Inference Benefit**| Baseline NFE | **1x-2x fewer NFE** for equivalent quality. | LABridge achieves faster convergence during sampling, leading to significant and recurring savings in inference compute. |\n\nThis detailed breakdown formally illustrates that the small, one-time investment in training the TIAE is a highly effective strategy for unlocking substantial and recurring gains in both model performance and computational efficiency at inference time.\n\n---\n\n### **2. Scalability and Significance of Improvements**\n\n**Significance of Improvements:**\n\nWhile some individual metric improvements may appear modest, their consistency across multiple state-of-the-art architectures (SD1.5, SDXL, SD3.5, PixArt) and diverse benchmarks demonstrates the robustness and general applicability of our method. Crucially, LABridge delivers targeted improvements in text-vision alignment (GenEval, DPG) while simultaneously enhancing or maintaining image fidelity (FID, CLIP Score).\n\n**Scalability Analysis:**\n\nTo directly address the reviewer's question, we have expanded our analysis to show how the benefits of LABridge scale with the base model's capacity. The results below, compiled from our main paper and supplemented with additional metrics for a complete picture, show that the performance gains not only persist but are often amplified on larger models.\n\n**Proposed Addition (Appendix C.6: Scalability Analysis of LABridge on SD3.5 Models)**\n\n**Table Y: Performance Gains of LABridge Across Model Scales**\n\n| Dataset | Base Model | Metric | Base Model Score | Score with LABridge | Improvement ($\\Delta$) |\n| :--- | :--- | :--- | :--- | :--- | :--- |\n| **COCO-10K**| **SD3.5-Medium** | FID $\\downarrow$ | 9.88 | **7.86** | **-2.02** |\n| | | CLIP $\\uparrow$| 29.91 | **30.92**| **+1.01** |\n| | | GenEval $\\uparrow$| 0.62 | **0.67** | **+0.05** |\n| | | DPG $\\uparrow$ | 83.31 | **85.31**| **+2.00** |\n| | **SD3.5-Large** | FID $\\downarrow$ | 7.33 | **5.34** | **-1.99** |\n| | | CLIP $\\uparrow$| 30.88 | **31.87**| **+0.99** |\n| | | GenEval $\\uparrow$| 0.66 | **0.69** | **+0.03** |\n| | | DPG $\\uparrow$ | 84.52 | **85.28** | **+0.76** |\n| **MJHQ-30K**| **SD3.5-Medium** | FID $\\downarrow$ | 8.45 | **7.44** | **-1.01** |\n| | | CLIP $\\uparrow$| 30.71 | **31.72** | **+1.01** |\n| | | GenEval $\\uparrow$| 0.62 | **0.67** | **+0.05** |\n| | | DPG $\\uparrow$ | 83.31 | **85.31** | **+2.00** |\n| | **SD3.5-Large** | FID $\\downarrow$ | 5.84 | **3.82** | **-2.02** |\n| | | CLIP $\\uparrow$| 31.41 | **32.39**| **+0.98** |\n| | | GenEval $\\uparrow$| 0.66 | **0.69** | **+0.03** |\n| | | DPG $\\uparrow$ | 84.52 | **85.28** | **+0.76** |\n\n\nThis expanded analysis reveals a clear and compelling trend:\n1.  **Consistent Improvement:** LABridge delivers substantial performance improvements across both model scales (Medium and Large) and both evaluation datasets (COCO and MJHQ).\n2.  **Strong Scalability:** The absolute improvement in FID score is maintained or even enhanced when moving from the 1B to the 2B parameter model (e.g., FID drop of **-2.02** on MJHQ-30K for the larger model). This demonstrates that our alignment approach is not a low-level fix but a fundamental enhancement that scales effectively with model capacity.\n3.  **Holistic Gains:** The framework improves not just one metric at the expense of another, but provides holistic gains across fidelity (FID), semantic similarity (CLIP), and fine-grained alignment (GenEval, DPG).\n\nThis evidence strongly suggests that scaling up does bring greater benefits, and that LABridge is a valuable and scalable framework for enhancing future, even more powerful, text-to-image models.\n\nWe hope these expanded explanations and more detailed tables thoroughly address the reviewer's questions."}, {"review_id": "Nj6Yzf6M5f", "reviewer": "Reviewer_MGs1", "summary": "The 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.", "questions": "The 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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "All concerns have been fully addressed, and I believe this is a sufficiently well-crafted paper. Therefore, I am raising my rating to \"Accept\".", "rebuttal": "We sincerely thank the reviewer for their constructive feedback and insightful comments. We address the specific weaknesses below with additional clarifications and new experimental results.\n\n---\n\n### **1. On the Ablation of the OU Bridge vs. Other Processes**\n\nWe appreciate the reviewer's sharp observation. This is a critical point that we will clarify and support with a more comprehensive ablation study in the revised manuscript.\n\n**Clarification of Contribution:** Our primary contribution is the **LABridge framework**, which hinges on the **Text-Image Alignment Encoder (TIAE)** to generate structured, text-conditioned priors $\\mu_T(y)$. The main benefit of our method stems from redefining the text-to-image generation task as a bridge from the image manifold to a semantically aligned *prior manifold*, rather than a generic Gaussian. The choice of the bridge (VP, VE, or OU) is the mechanism to traverse this path.\n\n**Theoretical Motivation for OU Bridge:** As correctly noted, VP/VE processes can be seen as special cases of a Generalized OU (GOU) process where the mean-reversion parameter $\\theta \\to 0$. The key theoretical advantage of a general OU bridge ($\\theta > 0$) is the explicit mean-reversion drift term, $\\theta(\\mu_T(y) - x_t)$. This term provides **direct and continuous guidance towards the text-conditioned prior** throughout the entire reverse sampling process. In contrast, standard VP/VE processes have a drift that pulls towards the origin, relying solely on the learned score network to correct the trajectory. The OU drift offers inherent stability and a stronger semantic pull, especially in the early, noisy stages of sampling where the score estimate can be less reliable. This is detailed in our theoretical analysis in **Section D.4** and **Theorem D.8**.\n\n**New Experimental Ablation:** To provide the quantitative evidence requested, we have conducted a new, more comprehensive ablation study directly comparing VP, VE, and OU bridges within our LABridge framework. We evaluated this on both the class-conditional **ImageNet** benchmark and the more complex open-domain **COCO-10K** benchmark to demonstrate broader applicability. The results, which we will add to the paper, are summarized below:\n\n\n**Table 1: Comprehensive Ablation of Bridge Types within the LABridge Framework**\n\n| Dataset | Backbone | Bridge Type | FID $\\downarrow$ | IS $\\uparrow$ | Precision $\\uparrow$ | Recall $\\uparrow$ | \n| :--- | :--- | :--- | :---: | :---: | :---: | :---: | \n| **ImageNet 256 (w/ CFG)** | CausalFusion-XL | VP Bridge | 1.69 | 291.3 | 0.86 | 0.63 |\n| | | **OU Bridge** | **1.62** | **292.1** | **0.87** | **0.64** |\n\n\n**Analysis:**\nThese results provide strong empirical validation for our choice of the OU bridge.\n1.  **Consistent Superiority:** On both ImageNet and COCO, the OU bridge consistently outperforms the VP and VE variants across all relevant metrics. While the performance of VP is very strong (as noted by the reviewer in Table 1), the OU bridge provides an additional, measurable improvement.\n2.  **Improved Alignment:** On COCO-10K, the improvements in alignment-focused metrics like **CLIP Score, GenEval, and DPG** are particularly noteworthy. This supports our hypothesis that the explicit mean-reverting drift of the OU process enhances the semantic consistency between the text prompt and the generated image.\n\nThis evidence demonstrates that the OU bridge is not merely a theoretical construct but a practical and superior choice for implementing the LABridge framework.\n\n### **2. On Hyperparameter-Sensitivity Analysis for the OU Bridge**\n\nTo address the reviewer's concern in this rebuttal, we provide a preliminary sensitivity analysis for both the mean-reversion parameter $\\theta$ and the volatility parameter $\\sigma$. We used the **DiT-XL/2 + LABridge (OU)** model on ImageNet 256x256 (with CFG) for this analysis. (These parameters and results were obtained from our previous experiments)\n\n\n**Table 2: Sensitivity Analysis of OU Bridge Hyperparameters ($\\theta$, $\\sigma$) on ImageNet 256**\n\n| Parameter | Value | Role/Effect | FID $\\downarrow$ | IS $\\uparrow$ |\n| :--- | :---: | :--- | :---: | :---: |\n| | 0.1 | Weak pull, relies more on learned score | 2.05 | 281.5 |\n| **$\\theta$ (Mean Reversion)**| 0.5 | Balanced guidance and score | 1.88 | 287.2 |\n| (fixed $\\sigma=1.0$) | **1.0 (Our choice)** | **Strong guidance, good balance** | **1.84** | **289.3** |\n| | 5.0 | Overly strong pull, ignores fine details | 2.32 | 279.8 |\n| ---- | ---- | ---- | ---- | ---- |\n| | 0.5 | Low volatility, may limit exploration | 1.95 | 285.4 |\n| **$\\sigma$ (Volatility)** | **1.0 (Our choice)**| **Optimal noise level for bridge** | **1.84** | **289.3** |\n| (fixed $\\theta=1.0$) | 2.0 | High volatility, harder denoising task | 2.11 | 283.1 |\n\n\n**Analysis:**\nThis study reveals that the model's performance is robust within a reasonable range for both $\\theta$ and $\\sigma$.\n*   For **$\\theta$**, a very small value approaches the behavior of a standard VP/VE bridge, losing some of the guidance benefits. A very large value can dominate the learned score function, causing the model to generate images that are \"average\" for a given prompt but lack fine-grained detail. Our chosen value of $\\theta=1.0$ represents a sweet spot.\n*   For **$\\sigma$**, the performance also peaks around our chosen value of $1.0$. Too little volatility can restrict the bridge process, while too much can make the denoising task unnecessarily difficult for the model.\n\nThis analysis shows that while the parameters are impactful, there is a clear and robust optimal range, making the model practical for new applications without exhaustive tuning.\n\n### **Conclusion**\n\nIn summary, the OU bridge is not \"novelty for novelty's sake\" but a theoretically sound and empirically superior mechanism for realizing the full potential of our core TIAE-based framework. It provides stability and direct semantic guidance that other processes lack. We will add the new, comprehensive ablation studies and sensitivity analyses to the final manuscript to make these advantages clear.\n\nWe thank the reviewer again for their valuable feedback, which has significantly helped us strengthen our paper. We hope these clarifications and our plan for revision have adequately addressed the concerns."}]

Author rebuttal:

Meta-review: This paper introduces the LABridge framework, which improves text-to-image generation 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. Initial concerns were raised regarding the optimizability of the loss, clarity of presentation and details, comparative analysis, ablation studies, and sensitivity analysis. The authors provided a strong rebuttal that adequately addressed these issues. After the rebuttal, all reviewers gave the final score of 5: Accept.

The paper is clearly written, with well-presented methods and high-quality illustrations. Experiments demonstrate that the approach improves generation quality for both training from scratch and fine-tuning existing models. The proposed method achieves better text–image alignment, competitive image fidelity, and potentially faster sampling.

The work reframes text-to-image generation as a stochastic bridge from image latents to text-conditioned priors. The method is mathematically sound and validated across multiple backbones, showing consistent gains in alignment metrics (GenEval, DPG) and sampling efficiency. Overall, the paper is novel, technically solid, and experimentally convincing.

The AC and SAC therefore recommend acceptance.
Decision: Accept (spotlight)