Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation
Authors: Shufan Li
Jiuxiang Gu
Kangning Liu
Zhe Lin
Zijun Wei
Aditya Grover
Jason Kuen
Venue: ICLR 2026 Poster (2026)
Keywords: Masked Diffusion Model
Unified Multi-modal model
Primary area: foundation or frontier models, including LLMs
TL;DR: We built a state-of-the-art unified masked diffusion model for image understanding ,object grounding, image generation and editing tasks.

Abstract: We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation.  Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a lightweight generation branch with a larger understanding branch, supported by token compression,  universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting generation quality with its understanding capabilities. Lavida-O achieves state-of-the-art performance on a wide range of benchmarks including RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference. These advances establish Lavida-O as a new paradigm for scalable multimodal reasoning and generation.

Published as a conference paper at ICLR 2026
LAVIDA-O: ELASTIC LARGE MASKED DIFFUSION MODELS FOR UNIFIED MULTIMODAL UNDERSTAND- ING AND GENERATION
Shufan Li Jiuxiang Gu', Kangning Liu', Zhe Lin', Zijun Wei*
Aditya Grover Jason Kuen'
Adobe UCLL
Work done primarily during internship Adobe Research
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Figure 1: We propose Lavida-O, a unified large masked diffusion model capable of multi-modal understanding and generation.
ABSTRACT
We propose Lavida-O, unified Masked Diffusion Model (MDM) for multi- modal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents single frame- work that enables image-level understanding. object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Cocuur--o--Trasfoomme (Elastic-MoT) architecture that couples a lightweight generation branch with larger understanding branch, supported by token compression, universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting genera- tion quality with its understanding capabilities. Lavida-O achieves state-of-the-art

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performance a wide range of benchmarks including RefCOCO object ground- ing, GenEval text-to-imase image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5- VI. and FluxKontext-dev, while offering substantial speedup at inference. These advances establish Lavida-O new paradigm for scalable multimodal reasoning and generation. Code and checkpoints are available at research/La Vida-O
INTRODUCTION
The abilities to understand and generate images have been two essential objectives of image mod- eling research. Traditionally, these tasks are handled by a diverse of specialist models, such as detection models for object localization (Liu et al. 2024b ei al.
(023a), Visual Question Answer- ing (VQA) models for question-answerrng (Li et al. 2022), and di diffusion models for text-to-image
generation (Esser et al. 2024 Podell et al. 2023 Rombach et al. 2022). Recently, the rise of uni- fied multi-modal models such as GPT-4o [OpenAl] 2024) has introduced a new paradigm: using a single generalist model to perform a wide range of image understanding and generation tasks. Not only is this unified approach more aligned with the goal of developing versatile multi-task Artificial General Intelligence (AGI), but it also demonstrates strong empirical performance by allowing un- derstanding and generation capabilities to mutually benefit each other (Deng et al.] [2025). This is especially notable in tasks requiring both understanding and generation capabilities, such as image editing, where unified models show unparalleled advantages over generation specialists.
Most current unified models are built on Autoregressive (AR) large language models. Some works, such as BLIP3o (Chen et al., 2025a) and BAGEL (Deng et al. 2025). employ AR modeling for text generation and continuous diffusion modeling for image generation (AR+ while others, such as Janus (Chen et af. 2025c), first tokenize images into sequences of discrete tokens and then employ unified AR next-token prediction objective for both image and text modalities.
Recently, Masked Diffusion Models (MDMs) (Lou et al. 2023; Sahoo et al., (024) have emerged as a competitive alternative to AR models. Unlike AR models, MDMs treat token generation diffusion process over discrete tokens. In the forward process, the tokens of a sequence are gradually masked, At inference, we start with sequence of mask tokens and gradually unmask them obtain a sequence of meaningful tokens. Large-scale experiments in language modeling (Nic et al. [2025] Ye et al. 2025a) show that MDMs can achieve comparable performance to AR language models while offering many advantages, such as better speed-quality tradeoffss controllability, and bidirec- tional context. Several recent works extend MDMs multi-mode understanding and generation tasks (Li et al., 2025a: Yu et al., 2025b Yang et al. 2025; [hh et al. 2025). Compared with the +R+diff setup, unified MDMs avoid the need to carefully tune the balance between AR and diffu- sion losses by offering unified objective, resulting in greater simplicity and scalability. Compared with unified AR modeling, unified MDMs offer significantly faster sampling speeds by allowing parallel decoding of multiple tokens.
Despite these advantages, the latest unified MDMs such as MMADa YYng et al. 2025) and Muddit (Shi et al. 2025) still lag behind oftte-of-theaarr unified AR and AR+diffusion models, both in the breadth of tasks they support and in benchmark performance. There are three main challenges in developing high-performing unified MDMs. First, unified models are expensive to train due to the large size of their language backbones. For example, to build a unified MDM with image generation capability, MMaDa pretrains an 8B model jointly on text and image generation, which is costly. This challenge is further exacerbated by the limited literature on training large-scale masked image generative models. In contrast, many open-source large-scals scale continuous diffusion models such as Flux (Labs) 2024) are readily available. Second, open-source resources for masked image generative models (MIGMs) are scarce, and the literature on their training techniques and sampling processes is less developed than that for continuous diffusion models. Even the best open-source MIGM, Meissonic- 1B (Bai et al. 2024), significantly underperforms continuous diffusion models of comparable size (Xie et al. 2025a). Lastly, while these models can perform both understanding and generation tasks, they lack explicit mechanisms to leverage image understanding capabilities to improve generation quality. In fact, MMaDa and Muddit cannot even perform image editing tasks,
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which require both understanding and generation capabilities. These models simply concatenate text-to-image data and image understanding data during training.
Masked Diffusion Model (MDM) capable of both image understanding and generation tasks. To mitigate the cost training large diffusion models, Lavida-O introduces several techniques such as Elastic Mixture-of-TTansfommee Transformers (Elastic-MoT), progressive upscaling (gradually increasing the image resolution during training), and token compression that enable efficient scaling. To improve generation quality, Lavida-O em- ploys stratified sampling and universal text conditioning. To fully leverage the potential of a unified multi-modal model, Lavida-O incorporates planning and self-reffee ceff-reflection mechanisms that explicitly utilize its understanding capabilities to enhance generation outputs. We highlight Lavida-O's 's capa- bilities compared with previous multi-modes MDMs in Table
To bridge this gap, propose Lavida-O, unified
Through extensive experiments, we show that Lavida-O achieves state-of-the-art performance wide range of benchmarks such as RefCOCO object grounding (Kazemzadeh et al. 2014), GenEval text-to-image generation (Ghosh et al.) 2023), and ImgEdit (Ye et al. (025b) image editing, outper- forming existing autoregressive and continuous diffusion models such as Qwen2.5-VL. (Bai et al.) 2025) and Flux .1 Kontext dev (Labs et al. (025). while offering up to 6.8% speedup. Overall, our contributions can be summarized as follows:
We propose the first multi-modal MDM that achieves state-of-the- performance on text- to-image generation, image editing, and grounding tasks, outperforming existing MDMs, AR models, and continuous diffusion models.
We propose several efficient and effective training and inference techniques for large-scale masked image generative models and unified multi-modal models, such as the Elastic-MoTT architecture, universal text conditioning, and stratified sampling, significantly advancing the literature.
We introduce novel paradigm that explicitly leverages the understanding capabilities of unified model to improve its generation through planning and self-reflection.
Table I: Capabilities of different multimodal MDMs. Lavida-O uniquely supports localized un-
derstanding, high-resolution image synthesis, image editing and interleaved generation.
Understanding
Generation
Model
Image-level Object-level
Text-to-image
Image-editing Interleaved
LaViDa, Dimple, LLaDa-V
X
X
x
Muddit
5122
MMaDa
/
512
LaViDa-O
102442
2 BACKGROUND AND RELATED WORKS
2.1 MASKED DIFFUSION MODELS
Masked Generative Modeling (MGM) has emerged
alternative to AR models for modeling
sequences of discrete tokens. Early works such as BERT (Devlin al. 2019) used MGM as rep- resentation learning objective. Later works (Chang et al.) 2022 2023) such as MaskGIT explored using MGM for generative modeling. In this setup, a sequence is initialized with only mask to- kens, which are then gradually unmasked to generate the desired output. In these works, discrete tokenizers like VQGAN (Esser et al. 2021 are used to convert images into discrete tokens.
More recently.MDMs, (Austin et al. 2021 Sahoo al.) 2024 Lou et al. 2023) have further devel- oped the theory of MGM by formalizing the masking and unmasking process as the forward and reverse diffusion processes in discrete space. This provides principled framework for training and
MMudit showed examples of simple editing through inpainting. It does not have instruction- based editing capabilities.
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sampling from these models. MDMs have renewed interest in masked modeling for language gen- eration, offering theoretical advantages over AR models, such as better speed-que queediquality tradeoff's and improved controllability. Notably, LLa and Dream-8B (Nie al. 2025 Ye ct al. 2025a)
demonstrated that MDMs can achieve competitive performance compared to AR models at scale. Several follow-up works (Li et al. 2025a Yu al. 2025b] You al.
2025 Yang al. 2025)
such as Va extend MDMs to multi-modal tasks such as image understanding and lext-to-image
generation. Their capabilities are summarized in Table |
Formally, given a sequence of L discrete tokens Xo [X] q(x, xx) gradually masks the tokens over the time interval [O.1]. with 2 2 s 2 O. At 1, the sequence X1 consists entirely of masked tokens, denoted by [M.] A neural network pe is used to model the reverse process p( xxx) (x). The masked diffusion objective is defined as:
.,....., X the forward process
LMDM -Bt,No.x, [l
where Po (o Xx) is factorized into IT Po (X)] based on independence assumptions (Sa-
hoo et al. 2024). At inference time, the model starts from fully masked sequence X1 [M, M,, M] and progressively applies the learned reverse process lo to recover the original tokens. We provide more detailed formulation of MDMs Appendix A.
2.2 UNIFIED MULTI- MODELS
capable of both image understand- ing and generation tasks, leading to strong performance on tasks requiring both capabilities, such as image editing. Generally, there are two dominant types of unified models based on their modeling objectives. The first type, such as BAGEL (Deng et al. (025). employs an AR objective for text gen- eration and diffusion objective for image generation (AR+diff). However, this design involves two different training objectives with distinct numerical scales and training dynamics, often requiring careful tuning of loss weighting and data mixtures. In contrast, the second type of models employ unified objectives for both image and texts. Early works like Janus-Pro (Chen et al., 2025c) employ unified AR modeling objective. Recent works like UMDD and MMaDa (Yang et al. 2025) Wang & Shi 2008) explore a unified MDM objective. Despite some success, significant performance gap remains between these unified MDMs and state-of-the-art unified models in the AR and AR+diff categories.
Unified multi-modal models such GPT-4o OOpenAl 2024)
Architecturally, unified models also fall into two main categories. The first type, such as Janus and MMaDa, uses single dense transformer to output both image and text tokens. The second type, such as BAGEL and MetaQueries (Pan et al., 2025), employs separate parameter sets for handling image and text modalities. A common design in this category is the architecture (Liang et al.] 2024), where image and text inputs are processed by different parameter sets but can interact through joint attention mechanisms. Under this paradigm, several works such as X-Fusion and LM-Fusion (Mo et al. 2025 Shi et al. 2024 further investigated architecture designs and training recipes of MOT. These designs are illustrated in Figure While being more flexible, training MoT experts can be expensive due to their large parameter counts.
(MoT)
Under
3 METHOD
3.1 MODEL ARCHITECTURE
Lavida-O's model architecture is built on LaViDa (Li et al. 2025a), diffusion model capable of only image understanding tasks. LaViDa uses SigLIP (Zhai et al. 2023) vision encoder to convert input images into continuous semantic embeddings C, which are concatenated with token embeddings of text prompts C2 form the final conditional embeddings C = Concat(C,, Cp) for visual understanding tasks. At each inference step, the diffusion model uses the partially unmasked answer X, and the conditional embedding C' to predict the clean text answer Xo-
For image understanding tasks, Lavida-O maintains this exact setup of LaViDa. To incorporate visual generation tasks, we extend LaViDa's design by representing target images sequences of
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Figure 2: Overall Pipeline of Lavida-O. Given input image and text prompt, we first concate- nate the image semantic embedding C, image VQ embedding C,, and text prompt embedding Cp form the conditioning embedding C. The combined embedding then passed to the model along- side the partially masked sequence Xg. The model then predicts the full--unmasked sequence Xo.
discrete tokens using a VQ-Encoder (Esser et al., 2021 When performing these tasks, Xo and X, contain not only text tokens, but also VQ tokens that represent images. For image editing and interleaved generation tasks, additionally incorporate VQ tokens of input images C. part of the conditional embedding C' Concat(C, C, C.., Cp). since using semantic embeddings C₁ alone can degrade the low-level details needed for editing. To reduce the number of tokens and improve computational efficiency, we introduce a token compression module that reduces the number of VQ tokens by factor 4. The overall pipeline is illustrated Figure
3.1.1 ELASTIC
(ELASTICMOT)
Our goal is to find an efficient method that can equip understanding-oni, diffusion model with visual generation capabilities. However, both of the existing common choices described Section 2.2 dense models and MoT-aare very expensive. Dense models use the same set of parameters for all tasks, requiring a mix of understanding and generation data during training to prevent catastrophic forgetting, which is not data-efficient. While the MoT setup allows freezing the understanding branch and training only the generation branch for image generation, its architecture doubles the total parameter count, leading to considerable computational overhead. Moreover, given an 8B base understanding model, both setups require training at least 8B parameters for generation tasks from scratch, which is prohibitively expensive.
To address these limitations, we propose Elastic-MoT,, a novel architecture design that efficiently adapts an understanding-only model for image generation tasks. Compared with the vanilla MoT architecture, Elastic-MoT introduces two major modifications. First, instead of using equally sized branches, the generation branch has smaller hidden size. This reduces the parameter count and enables efficient training. We make this design choice based on the observation that many text-to- image models can generate high-quality images with only 2-4B parameters, suggesting that gener- ation tasks may not require much capacity understanding tasks (Xie et al. 2025a b).
Second, given an --layer model, instead of having joint attention all layers, we only allow text and image modalities to interact in the first M layers. In the remaining K = N M layers, text and image tokens interact only within their modality through self-attention. This design activates only partial parameters for different tasks. For example, in Lavida-O's final design, the generation branch has 2.4B new parameters and the understanding branch 8B parameters from LaViDa. With N = 32 layers and M K 16, image generation activates only 6.4B parameters (2.4B from generation + 4B from the first 16 understanding layers). During text-to-image pretraining, only the 2.4B generation branch is trainable, further improving the efficiency. Similarly, understanding
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Amertion
Atwerion
x
Gen.
Und
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Gen.
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KN
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X
M
Gen OKY
MMaDA (Dense)
Bagel (MoT)
LaViDa-O (ElasticMOT)
88
7B+78
2.48+48 48 (Text-tee to-l Task)
Active
8B (Understanding Task! (Interleaved Task)
Active
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Figure 3: Design Elastic MoT. Elastic-MoT introduces two major modifications to standard MoT. First, the generation branch has smaller hidden size. Second, given an N-layer model, only allow text and image modalities to interact in the first M layers. These two designs allow us to flexibly load only portion of parameters depending on tasks, improving the efficiency.
tasks use 8B active parameters, while interleaved tasks requiring both branches use 10.4B. The full Elastic-MoT design is shown in Figure 3 with further details in Appendix A.2 and B.2
3.1.2 MODALITY-AWARE MASKING
One of the challenges in adapting MoT architecture for MDMs is routing the mechanism to deter- mine which branch should be activated for each token. This is trivial for unified AR MoT models, where the model can simply learn to generate special token (e.g., [img-start]) to indicate that next token should use the generation branch. However, MDMs decode tokens in parallel and must decide in advance which mask tokens should be routed to the understanding branch and which to the generation branch. naive solution is let the user specify the number and location of text and image tokens, but this is difficult for interleaved generation, such as image generation with self-reflection. To address this issue, we design modality-aware masking process.
text tokens and N image VQ tokens, the vanilla forward diffusion process gradually converts it into M + N mask tokens during the time interval [o, 1]. By contrast, modality-aware forward process introduces a special timestamp lexp E [o, 1]. at which fully masked image VQ tokens are collapsed into special [exp] text token. This process is illustrated in Figure (Bottom-up). At inference, we assume all mask tokens are text tokens at the beginning. When [exp] token is generated, we replace with a sequence of Ling mask tokens, and specify that these tokens will be processed by the generation branch for image synthesis in subsequent forward calls. This process also illustrated Figure 4a (Top-down). We provide additional details Appendix
Given a sequence
4a
A.3
3.2 TASK-SPECIFIC DESIGNS
In this section, we describe several additional technical innovations that improve the effectiveness and efficiency on newly incorporated tasks such as image generation, image editing and grounding.
Universal Text Conditioning. A common approach to improving the quality of text-to-image mod- els is micro-conditioning (Podell et al.) 2023). which conditions the image generation process on extra parameters such original image resolution, crop coordinates, and image quality scores. This typically achieved via specialized embeddings. However, since a unified model has strong lan- guage understanding and reasoning capabilities, can simply append these conditions plain text (e.g., "SCORE: 5.40") to the end user prompts. In addition to common conditions, we also incorporate image luminance and contrast micro-conditions. This simple and effective design not

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Figure 4: Design choices of Lavida-O. (a) Forward diffusion process with modality-award masking. (b) Visualization of the unmasking order in the proposed stratified random sampling process.
only improves image quality by biasing generation toward high-scoring distributions, but also gives users more refined control over outputs. We provide additional details Appendix
Stratified Random Sampling. Most MDMs use confidence-bee basedd sampling, unmasking high- confidence tokens first. In image generation, high-confidence tokens tend cluster around already unmasked tokens. This negatively affecting image quality because adjacent tokens are highly corre- lated, which contradicts the independence assumption of MDMs. To mitigate this, we introudced stratified sampling process. Starting with a 2 2 grid, we unmask one token per region to ensure broad spatial coverage. Each region is then recursively subdivided into four smaller subregions, and continue unmasking revealed, producing a balanced, evenly distributed unmasking pattern across the entire image. This illustrated in Figure 4b More details and analysis provided in Appendix A.5 and B.3
token from each
region. This process repeats until all tokens
Planning and Reflection. While existing unified MDMs integrate image understanding and gen- eration tasks with single objective, they do not incorporate mechanisms that use understanding to improve generation, except for the assumption that joint training benefits both tasks. To address this, introduce two explicit mechanisms that leverage understanding to improve generation: planning and reflection. With planning, the model first generates a layout of the image represented by bound- ing boxes. then creates the actual image accordingly. For image editing tasks, it first identifies the desired edit region before generating the edited image. With reflection, the model evaluates its own generation using understanding capability and determines whether it satisfies the user's request. If misalignment is detected, the model generates a new image correcting the error. Examples are shown in Figure ! with additional technical results and analysis in Appendix A.7 and B.5
Object Grounding with Coordinate Quantization. The bi-directional context of MDMs naturally allows parallel decoding bounding box coordinates. While Lavida-O can represent numbers as plain text, adopt specialized scheme that normalizes all bounding box coordinates to [0. 1] T 1021
and quantizes them into 1025 discrete tokens representing 1024 T024 .... 1024 This ensures each bounding box is represented by exactly four tokens. At inference, we construct multiple query input with masked tokens such as "A dog [m][m][m][m]] A cat coordinates in parallel. This design allow to decode multiple bounding boxes in low single diffusion step, greatly boosting the efficiency. We provide further details in Appendix A.6
and unmask all
4 EXPERIMENT
4.1 SETUP
We start with LaViDa (i al. 2025al and extend it with 2.4B image generation branch using the ElasticMoT architecture described in (oction3..1.] The training consists of three stages: Stage 1: We continue training the base model object grounding and image-level understanding tasks.

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We incroprate 2.4B image generation and pretrain for text-to-image generation. We start with a resolution of 256 and progressively increase to 512 and 1024 during training. Stage 3: In the final stage, we jointly train the entire 2.4B + 8B model end-to-end on image understanding, text-to-image generation, image editing, and interleaved generation tasks such as planning and self- reflection. More details on the training data and process are provided in Appendix [B.
Stage 2:
4.2
MAIN RESULTS
Image Understanding. We report the performance of image understanding tasks in Table Lavida- O outperforms the previous state-of-the-ard unified diffusion model, MMaDa, by considerable mar- gin on MMMU (Yue et al., 2024), MME (Fu et al., 2023). and MMB (Liu et al. 2024c). Compared with the base model La ViDa, Lavida-O achieves substantial improvements on most benchmarks
such as ChartQA (Masry ct al. 2022), DocVQA (Mathww et al. (021)) ScienceQA (Lu ct al. 2022
and Math Vista (Lu al. 2023). due the scaling of the training data.
Table 2: Quantitative results on image-level understanding tasks. $ Evaluated by us.
Model
MMMU MME-P MME-C MMB ChariQA DocVQA Info Se.QA A12D M.Vista
AK Und. Only
LLaVe-1.6-7B Liu et 20244a
35.1
1519.3
323
54.6
64.9
74.4
37.1
73.2
66.6
34.4
14.3
Qwen2.5.V1-77 7B-78100 Ban 2124
58.6
83.5
84.9
82.6
83.9
68.2
49.2
Intern Inter--VL.3. V1.3-8B
am 21244
65.6
83.4
86.6
92.7
76.8
85.2
75.2
39.8
Unified
Gen.
BAGEL Deng al 2025
55.3
1687
701
85
73.1
Janus 1B ben et
2025c
36.3
1444
75.5
UniGen 1.50
32.3
79.4
67.4
44.6
Show-O Xie
2024
27.4
1233
Masked Only
Dimple Yu al
2025b
45.2
1514
432
74.6
63.4
77.1
74.4
42.3
LaViDa
La et
00230
43.6
1366
341
70.5
64.6
59.0
34.2
80.2
70.0
44.8
27.2
Masked United Und.
Muddit Shi al
2025
1104
MMaDa Yang
242
68.5
14.9
66.6
13.5
LaViDa-O
45.1
1431
488
76.4
80.0
73.7
44.6
84.6
76.7
56.9
36.9
Text-to-Image Image Generation. We report lext-to-image generation results on the GenEval (Goss 2023) and DPG (Hu et al. 2024) benchmarks, and FID scores on 30k prompts from the MJHQ (Li et al. 2024b) dataset. We compare against text-to-image models including Flux-dev
(Labs 2024). SD3- Medium (Esser 2024), Meissonic (Bai al. 2024) and DALLE-3 (OpenAI,
2023). unified models such BAGEL (Deng et al.) (225). MMADa (Yang et al. 2025) and Muddit Shi et al., 2025). Lavida-O significantly outperforms the state-of-the-an Meissonic masked image generation model, as well unified models such as MMADa and Muddit. Planning and reflection further enhance prompt-followee performance. We did not activate plamning and reflection on MJHQ due to its large size and that FID does reflect prompt-following capabilities.
Table 3: Quantative results on text-to-image generation tasks. *Evaluated by us. For fair comparison, we report results of UniGen after SFT stage.
Method
Parms.
Type
GenEval 1
DPG-BenchT
FID-30KL
Gen. Only
Flux-dev
Labs 2024
128
Continuous
0.68
84.0
10.15
SD3-Medium
Continuous
DALLE-3
UpenAl
2025
Continuous
0.67
83.5
Meissonic
Ba el al.
2023
Musked
0.54
Unified Und. and Gen.
BAGEL
Deng et
2025
Janus- Pro. H hen el
41755
AR
0.73
82.6
UniGen-
[Tán ct al.
2025
0.631
82.8 f
OmniFlow
20290
3.4B
Continuous
0.62
Show-o
Xig el al.
2024
1.3B
Masked
0.67
15.18
Muddit
Masked
0.61
MMaDA
Yang 2l.
2025
8B
Musked
0.63
53.4"
32.85*
4B++2411
Planning
8B+2.4R
Masked
0.85
82.9
*Reficction
8B+2.4B
Masked
83.2
Object Grounding, We evaluate the object grounding capabilities of Lavida-O on RefCOCO Refer-
ring Expression Comprehension (REC) tasks (Yu et al. 2016 Mao et al. 2016), reporting the Preci-
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sion @0.5 metric. Lavida-O outperforms autoregressive lingon- models such Qwen2.5- VL-7B (Bai et al. 2025) and IntemnVL--8B (Zhu et al. 2025). as well as specialist models such as Grounding DINO-L (Liu et al. 2024b and Seg ML-B (Wang al. 2025a).
Table 4: Precision 0.5 on ReICOCO, RefCOCO+, and RefCOCOg REC tasks.
Model
RetCOCO
ReCCOCO+
ReCOCCOg
TestA
test
S 7B Wang (2025g)
90.0
92.1
86.2
82.2
K5.5
76.1
83.9
85.9
Qwen2.5 VI [Ba et al.
20125
90.0
92.5
85.4
84.2
89.1
76.9
87.2
87.2
GroundingDINO
2024h
90.6
93.2
88.2
88.2
89.0
75.9
86.
87.0
IntemVL3--88 (Zhu orai 2025
92.5
94.6
88.0
88.2
92.5
81.8
89.6
90.0
(4-stpp)
LiViDa-O
91.9
94.6
88.4
87.4
91.7
82.2
89.5
89.8
Image Editing. We report image editing results on ImgEdit benchmark YYe al. 2025b) in Table
Lavida-O outperforms state-of-the-ar unified models such as BAGEL and specialized models like FluxKontext-dev. Most notably, Lavida-O even outperforms the state-of-the- closed-source model replacing and removing objects, which requires localized understanding. This underscores the effectiveness of Lavida-O's design integrating object- grreectggoudiigg capabilities.
GPT4-o(OpenAI
(2024) 20224
Table 5: Per-Category and overall scores ImgEdit benchmark.
Model
Add
Adjust
Extract
Replace
Remove
Background
Style
Hybrid
Action
Overall
GPT-4o OpenAl
2024
4.61
4.33
2.90
4.35
3,66
4.57
4.93
3,96
4,89
4,20
Owen2. Owen25VL=FFut Waig at 2025b
4,07
3.79
2.04
4.13
3.89
3.90
4,84
3.04
4.52
3.80
FluxKontex
3.76
OmniGen2 Wy W al A0460
3,57
3.06
L.77
3.74
3.20
3.57
4.81
2.52
4,68
3.44
UniWorld Li al 2025
3.82
3.64
2.27
3.47
3.24
2,99
4.21
2.96
2.74
3,26
BAGEL (Deng
3.56
3.31
1,70
3.30
262
3.24
4.49
2.38
4.17
3.20
StepIX IX- ml
2025
3.88
3.14
1.76
3.40
2.11
3.16
4.63
2.64
2.52
3.06
OmniGen Xiao
3,47
3.04
L.71
2.43
4.19
2.24
2.96
Ultrabdit (Zigo et
2022
3.44
2.81
2.13
2.96
1.45
2,83
3.76
1.11
2.98
2,70
Anylidit Yu
InstructAny Piy
al
20236
2.55
1,83
2.10
2.54
1.17
2.01
3,51
1.42
2.12
MagicBrush (Zhan et
2046
2.84
1.58
1.51
1.97
1.58
1.75
2.38
1.62
1,22
1,90
Instruct. Pix2Pxx(loo
2023
2.45
1.83
1.44
2.01
1.50
1.44
3.58
1.20
1.46
1.88
LaViDa-O
4.04
3.62
2.01
4.39
3.98
4.06
4.82
2.94
3.54
3.71
Planning
2.04
4.3 TRAINING AND INFERENCE SPEED
In Figure we benchmark the inference efficiency of Lavida-O across three tasks: text-to-image generation, object grounding, and math reasoning. We measure end-to-end latency in seconds per image. Lavida-O significantly faster than autoregressive models. Notably, we achieve a 6.8x speedup object grounding tasks compared to Qwen2.5-VL-7B (Bai et al.) 2025). We also port training efficiency measured by per-step latency and compare our Elastic-MoT design with BAGEL-style standard MoT design, Elastic-MoT improves the training speed by x.17x. Specif- ically, reducing the size of generation branch leads to a speedup of 2.23 x, and decoupling the attention operation in the last 16 layers lead tional analysis on the speed-quality tradeoff at inference time in Appendix B.7 and analysis on the training efficiency of Elastic-Mo'T design in B.2
additional speedup of 1.44x, We provide addi-
CONCLUSION
In summary, we proposed Lavida-O, the first multi-modal masked diffusion model that achieves state-of-the-art performance on text-to-image generation, image editing, and grounding tasks compeeiiive with the best specialist models and autoregressive unified models. We also introduced a novel paradigm of interleaved generation, which explicitly leverages understanding capabilities to improve generation results in unified multi-modal model through planning and self- reflection. In developing Lavida-O, we proposed several efficient training and inference techniques, including the ElasticMoT architecture, universal text conditioning, and stratified random sampling, providing valuable insights for future work in masked diffusion models and unified multi-modal systems.

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Published as a conference paper at ICLR 2026
Text-to -lmaee
Grounding
Math Reasoning
Training
6FT4
MoT (Bagel Style)
BAGEL
Owen2. 5VL.78
Oper Newe-LLLWW-eett-ll
Flox.1 dev
(LaVibe-o)
De-D
LaviDa-D
LaV
1.5
Latency (s/image)
Latency (s/image)
Latency (s/image)
Latency (s/step)
Figure 5: Training and Inference Speed Lavida-O. We compare the end-to-end inference la- tency of Lavida-O on three tasks, well as pretraining efficiency measured by per-step latency.
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Haozhe Zhao, Xiaojian Shawn Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction-based ane-grrined image editing at scale. Advances in Neural Information Processing Systems, 37:3058-3093, 2024.
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, et al. Internv1:: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.1044 2025.
Le Zhuo, Liangbing Zhao, Sayak Paul, Yue Liao, Renrui Zhang, Yi Xin, Peng Gao. Mohamed Elhoseiny, and Hongsheng Li. From reflection to perfection: Scaling inference-time optimization for text-to-image diffusion models via reflection tuning. arXiv preprint arXiv:2504. 16080, 2025.
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A ADDITIONAL TECHNICAL DETAILS
A.1 FORMULATION OF MASKED DIFFUSION MODELS
Masked Diffusion Models (MDMs) model the generation process of discrete token sequences through a continuous-time Markov chain (CMTC). Formally, given sequence of discrete tokens Xu [X X²- ..... X6 of length the forward process x(,,)x,) gradually converts it into a se- over the continuous time interval [0, 1]. with 1 2 1 2 & 2 O. Each token X; belongs to a fixed-size vocabulary set V. In our setup, V consists of text tokens, image VQ tokens, and the special mask token [M]. This forward process is
quence of mask tokens [M] denoted by X X? ..... x1
formally defined as
Cat(X;; M). Ca + + M),, if [M],
if [
(2)
where Cat(-) denotes a categorical distribution, and M, X; X RI are probability vectors, with |V| denoting the vocabulary size. In particular, M is one-hot vector representing the mask token [M]. This forward process yields the following marginal distribution:
q (X:) Cat(X); x)x + tM).
(3)
MDLM (Sahoo et al. 2024 demonstrated that the posterior of the reverse process p (X,, Xo) has the following form:
[Cat(X X Xt), (Cat(X; t- X + M),
if i + [M] if if: = [M].
PXXIIX, Xi)
In practice, replace X with the neural network prediction pa (X) when sampling from the reverse process, which gives the following transition:
(X;; x Cat( X po (X MM), if i [M]
if if + [M]
X,(
(5)
Sampling process. At inference time, initialize X1 sequence of mask tokens, with X X² = ... = x = [M]. We discretize the continuous time interval [o. 1] into discrete timesteps 0- = to < with 1: = and end when we obtain a mask- sequence Xu. At each step, we sample each token position independently, assuming that po (X (x,,) factorizes as I Po(X; (X,,), following
tK = and iteratively sample X ~ (X( (x,,) using Equation We start
previous works (Nie et al. 2025 Sahoo et al. 2024 Lou et al. 2023).
Training process. At each training step. given clean sequence Xo. we sample random timestep
l E [0, 1] and obtain X, ~ q( (x())) through the forward process defined in Equation 3 The loss is then computed using Equation I from Section 2.1
In this section, we have documented the standard training and inference process for typical MDMs. Our modality-wware masking design introduces several modifications the above processes, which are described in Section 3.1.2 of the main paper. Additional details are provided in Appendix A.3
A.2 ELASTIC-MOT ARCHITECTURE
In this section, we document the detailed design of the Elastic-MoT architecture described in Section 3.1.1 As discussed in the main paper, the proposed Elastic-MoTT architecture has two key differences compared to standard MoT: a generation branch with variable size and decoupled joint attention in the later layers.
Variable-sized generation branch. In standard MoT models such as BAGEL (Deng et al. 2025 the generation branch is initialized as exact copy of the understanding branch. For models in the
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UNA.VU MLY
Gon DLD
Lxc.MEP
Self
Und ViL
Und MB
SanukuF
Gon CLP
Und HL
nn Attariion
XM
Ja Attrrtinn
Join Attandon
X
Image Understanding
Image Generation
Interleaved Task
4B+2.48
88422488
Figure 6: Activated parameters of Lavida-O under different task settings. Elastic-MoT Design allow Lavida-O to dynamically loads its parameters depending on the tasks. For understanding-onl tasks, we only load the 8B generation branch. For eox-tto-mmaee generation tasks, we load the first M 16 layers of the understanding branch, which consists of 4B parameters, and the full 2.4B generation branch. For interleaved tasks, we load all 2.4B+8B parameters.
7-10B scale, this leads to a substantial increase in parameter count and compute overhead, limit- ing the scalability of MoT models. Motivated by the success of many medium-sized, high-quality text-to-image generation models, we explore using a smaller generation branch in the Elastic-Moil design. Since we still want the modalities to interact with each other through the joint attention mechanism, important to keep the dimensions of the query and the key vectors consistent. We provide detailed breakdown of the parameter sizes in Table 66) To initialize the generation branch with dimensions smaller than the understanding branch, we truncate the weights of the understand- ing branch and copy them to the generation branch.
Table Comparison of understanding (Und) branch and generation (Gen) branch configura- tions. The projection sizes are in the format [output size, input size].
Und Branch
Gen Branch
Atlention 4096
norm
2048
q-proj-size
[4096, 4096]
[4096, 2048]
proj size
[4096, 4096]
[4096, 2048]
v-proj size
[4096, 4096]
[4096, 2048]
attn out
[4096, 4096]
[4096, 2048]
MLP
norm
4096
2048
input size
4096
2048
hidden size
12288
8192
output_size
4096
2048
Decoupled attention. In standard MOT, understanding and generation tokens can interact with each other in all N transformer layers through the joint attention mechanism. We decouple attention in the last K layers and only allow tokens of the same type to interact with each other. In the first M K layers, all tokens can still interact with each other in the standard MoT architecture. This design is motivated by two factors. First, it prevents text and image tokens from interfering with each other's representations in the later stages generation. Second, and more importantly, allows us to load only 4B out of 8B parameters for text-to-image generation tasks, greatly improving the scalability of pretraining while also reducing compute cost at inference time. We visualize the tasks, activate only
activated parameters for different tasks in Figure 6 For understanding-ont the understanding branch in all N M + K layers. For generation-only tasks, we activate the understanding branch in the first M layers and the generation branch in all N M K layers.
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For interleaved tasks with both text and image outputs, activate all parameters. In setup, choose M = = 16, which yields 32 layers in total.
A.3 MODALITY-AWARE MASKING
In this section, we provide details of the changes the training and sampling process introduced by modality-aware masking. as described in Section 3.1.2 Recall that in adapting the MoT architecture for MDMss one of the main challenges is routing tokens. In particular, while can easily decide which branch should process unmasked tokens based on whether they are image VQ tokens or text tokens, is difficult to make such decisions for masked tokens, especially in interleaved generation tasks where the final output contains both images and text. Modality-awan masking addresses this problem by processing all tokens with the understanding branch by default and dynamically deciding when and where to invoke the generation branch during the sampling process.
Sampling Process. For convenience, we denote masked tokens that will processed by the un- derstanding branch Mund and masked tokens that will be processed by the generation branch as Mgen- With this distinction, the routing policy becomes simple: all text tokens plus Mund are processed by the understanding branch, while all image VQ tokens plus Mgen are processed by the generation branch. We introduce a special text token [exp] to indicate when an image should be generated. When [exp] token is generated in the unmasking process, it is automatically replaced with a sequence of Mgen tokens. The number of Myen tokens representing each image is deter- mined by prespecified output resolution. These tokens then processed by the generation branch in subsequent rounds. For example, each 1024 x 1024 image is represented by 1024 VQ tokens. This process documented in Algorithm [ and illustrated in Figure (Left).
Algorithm Interleaved Generation with Modality-Aware Masking
Input: Initial Generation Length L, discrete timestamps 0 = to < t ... < tx = 1, prompt C Initialize <- K
2: Initialize X?:L_ Mund
3: io do
4: Sample Xt,,, (X,,, Xtx C) // Eq. 5
5: if a [exp] token is generated in X then
6:
7: Replace it with sequence of M tokens
These Mgen will he routed to the generation branch in subsequent rounds
8: end if
9: end for
return Fully unmasked sequence Xo
Training. A consequence of modality-awar masking is that the partially masked sequence X, will have varying length depending on t, making the loss described in Equation [ not directly applicable. In particular, when sampling from the forward process qX,, Xo), there special timestep fexp at which a sequence of VQ image tokens is collapsed into a single [exp] text token. As illustrated in Figure 7 (Right), when f < fexp- X, has shorter sequence length than Xo. To apply the loss properly, we construct new sequence X by collapsing all sequences of image VQ tokens into [exp] tokens in Xo. We then modify the loss in Equation I to the following:
LMDM -Er,No,
Σ (1I
log 1o Xx)
(6)
i
(oo
if E (Feup: 1) if E (0, texp)
where Xo
X66
This change is also highlighted blue in Figure 7 (Right).
Understanding-Onl. and Gemeration-Only Tasks. We activate modality-awan masking only for interleaved tasks, since these require both the understanding and generation branches our Elastic-MoTT architecture. For computational efficiency, we do not modality-aware masking for
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1-1
Pleii bcam ISm)
V2livage Takin
PPC
She VFTige
Loss
X
[=hop
eqquiriton
Pate
Ir]
Encl
Dod
Image
Loss
1+0
X
bna linal Eire
Inference
Training
Figure 7: Training and inference with modality-awand masking, We visualize the sampling process with modality-aware masking on the left and the training process on the right. During the training. the loss is applied either Xo X depending the value of t with respect to texp.
"4k, hd. photorealistic Ic wide shot ofa lone astronaut stand- desclate, allen planet, bathed glow binary swirls around their
Dust Highly detailed, 8K, sc-fi art, dromatic light- ORIGINAL WIDTH: 1024: ORIGINAL 10026 0:453; 14 CONTRAST: SCORE: 7 7,000;HPP HPS:3.25 3.221
Luminance 0.1
Luminance 0.2
Luminance 0.5
Luminance 0.7
Contrast 0.1
Contrast = 0.2
Contrast 0.3
Contrast = 0.4
Figure 8: Effect of Universal Text Conditioning, On the left side, we visualize the text format used in Universal Text Conditioning. the right side, we visualize generation results under different choices of universal text conditioning.
understanding-only tasks such as image captioning, or for generation-only tasks such as text-to- image generation (without planning and reflection). This allows to best utilize the flexibility of Elastic-MoT and avoid loading unnecessary model parameters.
A.4 UNIVERSAL TEXT CONDITIONING
Universal text conditioning is inspired by the micro-conditioning approach (Podell al. 2023 em- ployed in many text-to-image image models. These models interoperate special conditioning embeddings incorporate non-text conditions such as the original image resolution or aesthetic score. Since Lavida-O is unified model with mathematical reasoning capabilities, we can represent these con- ditions directly as plain text. In particular, we include source image resolution, crop coordinates, aesthetic scores (Schuhmann 2022), and HPS scores (Wu et al.) 2023), following existing works (Podell et al. 2023; Bai et al. 2024). Additionally, we incorporate luminance (brightness) and con- greater control over the generated images. Each condition is represented simple string of the form "[KEY] [VALUE]". During training, each condition is randomly dropped with some probability. At inference, users may specify all conditions only subset.
trast give
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4/1096 Unmasked
8/1066 Unmasked
16/096 Unmasked
64/1096 Unmesked
1024/4096 Unmasked
Stratified(Ours)
4/4096
Unmasked
16/4096 Unmasked
64/4096 Unmasked
1024/4096
Halton
4/4096 Unmasked
8/4036 Unmasked
16/4096 Unmasked
64/4096 Unmasked
1C24/4096 Unmasked
Uniform
Figure 9: Visualization of different sampling processes. We compare the unmasking order of the stratified sampler, Halton sampler, and uniform random sampler. Uniform random sampler produces the least desirable spatial pattern, with many unmasked tokens clustered together. Halton sampler less ideal than stratified sampler because does not guarantee perfectly stratified coverage. For example, when the number of unmasked tokens is 4, the upper-right quadrant remains unoccupied.
This design is illustrated in Figure 8 By modifying these universal text conditioning parameters at inference time, users can flexibly control various image properties such as brightness. Notably, when brightness and contrast are set to very high values, the generated images become highly stylized in order to satisfy the constraints.
A.5 STRATIFIED RANDOM SAMPLING
In this section, we provide detailed descriptions of the stratified random sampling process introduced in Section 3.22 In the vanilla sampling process described in Equation [5] each token is unmasked independently. In practice, this often leads to suboptimal generation quality. Instead of unmasking tokens randomly, several works adopt alternative sampling strategies in which the unmasking order
of tokens is determined by heuristics such the model's confidence at each token position (Nie)
ct al. 2025 Ye et al. 2025a; Chang et al., 2022).
In image generation, tokens with high confidence are frequently adjacent one another. As result, confidence-bassee unmasking tends to reveal many adjacent tokens in a single step. Since tokens that spatially adjacent often highly correlated, this violates the independence assumption Pe lx,,) I (X (x stated in Section A.1 To address this, we design a stratified sampling process that ensures the unmasked tokens are spatially dispersed. Specifically, we enforce that the first 4 unmasked tokens occupy the four quadrants of the image; the first 16 unmasked tokens occupy all 16 subregions obtained by dividing the image into 4 x grid; and so forth. The algorithm is formally described below:
Our design is inspired by the stratified sampling process commonly used in numerical integration and computer graphics. It also follows similar motivation to the recent Halton mask scheduler, which uses the low-discrepancy Halton sequence to ensure that unmasked tokens are spatially dis- persed (Besnier et al.) (025). We illustrate the differences among stratified sampling, Halton sam- pling, and uniform random sampling Figure 9 As shown in the figure, uniform random sampling produces the least desirable spatial pattern, with many unmasked tokens clustered together. Com- pared with our proposed stratified sampling process, Halton sampling is less ideal because does not guarantee perfectly stratified coverage. For example, when the number of unmasked tokens is 4, the upper-right quadrant remains unoceupied. The benefits of stratified sampling are also reflected in FID scores, which we document in Section B.3
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Algorithm 2 Stratified Unmasking Order
Input: Image size N N Output: a list of coordinates (i,j) indicating unmasking order
1: Initialize empty list O
2: for d d= 1,
logg .N do
3: Partition the image into 2d x 2dd grid cells
4: for each grid cell in random order do
5: if O ng g Ø then
6:
Sample (g)-3g) uniformly within cell g
7:
Append (g)) to O
8: end
9: end for
10:
end for
return ?
A.6 OBJECT GROUNDING WITH COORDINATE QUANTIZATION
In this section, we provide detailed descriptions of Lavida-O's design for object grounding tasks. Given image and referring expression describing object, the grounding task requires locating the described object in the image by predicting its bounding box coordinates.
autoregressive vision-language models such as Qwen2.5-VIL (Bai al., 2025), bounding boxes represented as plain text strings, such as "[123, 232, 300, 1021J". At inference, the coordinates are generated sequentially from left right. This design has several limitations. First, since the model only sees a padded and resized image. it is difficult for the model to predict absolute pixel coordinates that depend on the original resolution of the input image. Second, the sequential generation order slow and inefficient.
To address these issues, we normalize the bounding box coordinates and quantize them into dis- crete bins. Specifically, given an image of size H x W, we first pad it to square image of size D D. where
max H, W), and normalize the bounding boxes the padded image to the range [0.1] by dividing the raw pixel coordinates by D. This step makes the coordinates indepen- dent of the original input resolution. We then round each coordinate into 1025 bins representing 1021 1024 1024 1024 ..... 1024 and represent them with special tokens. This reduces the number of tokens needed to represent each bounding box to exactly 4. Finally, since Lavida-O is a masked diffu- sion model with bi-directional attention mask and parallel decoding capabilities, we can predict multiple bounding boxes simultaneously. For example, if we want to obtain the bounding boxes of both "a cute dog" and "a boy," can initialize text sequence "a cute dog [m][m][m][m]: a boy [m][m][m][m]" and perform parallel unmasking of multiple bounding box coordinates. This design illustrated in Figure TO
0
2
A.7 REFLECTION AND PLANNING
The unique advantage of unified understanding and generation models is that they can leverage their understanding capabilities to improve generation results. Several works on unified models show that simple joint training on a combination of understanding and generation tasks improves performance on generation tasks (Xie et al. 2024: Deng et al. 2023). particularly in ceeruction-followiiii capa- bilities. Lavida-O pushes this paradigm further by introducing two explicit mechanisms to exploit understanding capabilities: planning and reflection. At inference, these capabilities are invoked via specialized prompts, such "please generate layout design before creating the final image".
Planning. To improve prompt-following capabilities text-to-image generation, we ask the model to first generate layout design of objects, which consists of (object, bounding box) pairs, before generating the final image. Such interleaved generation is achieved through the modality aware aware masking process described in Section A.3 We illustrate this process in Figure [] (Top). As shown, planning enables Lavida-O to follow challenging and unintuitive prompts, such as "a horse above an "stronaut."
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cute dog [ssa]]aaa]]]on))]onna]
boy [00[[664[]24]]ooe]]
ships [4][ [486]
Parallel Decoding
Mask Diffusion Model
dog. [m]] [m] [m]]
bay [m] [m]]m]] [m][m]
(mi [m] [m]
Figure 10: Coordinate Quantization. We normalize bounding box coordinates into the range [0,1] and discretize them into 1025 bins. This ensures that each bounding box is represented by exactly tokens, allowing efficient parallel decoding of multiple bounding boxes in a single step.
Similarly, we can adopt planning for image editing tasks. Given an input image and edit instruc- tion, the model can first leverage its grounding capabilities to identify the regions that need to be edited before generating the edited image. This process illustrated in Figure II (Bottom).
Reflection. We can improve text-to-image generation performance by leveraging Lavida-O's under- standing capability to achieve self-critique and iterative self-improvement. Given an input prompt, the model first generates an image, then performs self-critique step to evaluate whether the gener- ated image matches the prompt. If does, the generation process terminates. Otherwise, the model generates revised image and attempts to fix the identified issues. This cycle is repeated until an image passes the self-critique process or the maximum number of rounds is reached. At each round, we also invoke the planning capability. Since Lavida-O's context length is limited to 8192 tokens, we truncate the history when necessary to include at most three rounds. This process illustrated in Figure II (Middle). Formally, the reflection process is defined by the following algorithm
Algorithm Iterative Image Refinement with Self- Reff-Refeectin Loop
Input: Text prompt P. Unified Model θ Max Iterations Output: Output image
1: Initialize I + Generate WithPlanningg(),
> Generate an initial image Obtain initial feedback
2: Fi x- GerTextFeedhack((e. P1
3: for = 2 N do
4: H4 + H4T 1,,
Construct History Context
5:
if' Run out of context limit of model θ then Truncate H. by removing early rounds
6:
7:
end if
8:
I GenerateWitthPanniing
H4)
> Generate a new image Obtain new feedback Stop if no more improvements
9:
F, + GefTexfFeedback(?? It)
10:
if F; = "" then
11:
end return
12:
13:
end for
14:
return IN
Similar designs and algorithms have been explored for generation-only models in the context of inference-time time scaling, such as Reflect-DiT et. 2025b)) and ReflectionFlow (Zhuo et al.) 2025). However, unlike these works, which require an external vasion-language model as reward model, Lavida-O uniquely unifies layout planning, self-critique, and iterative self-in improvepteeettttt in a single model through unified generation process.
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Text-to-lmage Generation with Planning
Sure should the objects in the following menner: <p-horse<ip> [21]j00012 217220
image of above mars, dramatic lighting ultra-realistic volumetric lighting, backlight
(eajees (B4 (O22)
Text-to-lmage Generation with Reflection
image incorrect There should be apple, but only 2 exists image. should this.
photo of three apples
Image Editing with Planning
Replace the dog rebot.
Sure should consider replacing the objects locations:
sp--tig (to be replaced
<ipp-
Figure 11: Interleaved Generation with Planning and Reflection. We provide visual examples of interleaved generation, including text-to-image generation with planning (Top), text-to-image generation with reflection (Middle), and image editing with planning (Bottom). We always enable planning during the reflection process. The layout traces is omitted in the middle figure for clarity and better presentation.
B ADDITIONAL EXPERIMENT DETAILS AND RESULTS
In this section, we document the details of the experiments for better reproducibility, including data pipeline, training hyperparameters, and compute cost. In addition, we also provide additional experimental results on the effectiveness of various design choices used by Lavida-O, such as the Elastic-MoT design, stratified sampling, and the data pipeline.
B. SETUP
Pretrained Weights. We use LaViDa (L et al. 2025a) to initialize the understanding branch and semantic encoder. For the VQ encoder, adopt the Meissonic encoder (Bai et al. 2024). The
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image generation branch is initialized from the truncated weights of the understanding branch, as described in Section A.2
Data Pipeline. Unlike many frontier models, model does not make use of proprictary images or documents. Our training data consists of the following components:
A: Text-to- Imagto--meeaaaaae Pairs. We source data from LAION-2B 2B (Schuhmann et al., 2022) and COYO- 700M (Byeon et al. 2022). We additionally include SA-1B (Kirillov et al. 2023), JourneyDE DB (Sun et al. 2023), BLIP3o-60k (Chen et al. 2025a). and ShareGPT4o- Image (Chen et al. 2025b). Each dataset is heavily filtered to remove NSFW prompts, low CLIP scores (Radford al. (02T). low aesthetic scores (Schuhmann, 2022) and low-resolution images. This results in 200M images in our final mix. Where available, we use captions generated by VLMs instead of raw alt-texts. These captions are sourced from existing work including Recap-LAION, Recap-COYO (Wu et al. 2024), and BLIP-3o (Chen et al. 2025a).
B: Image-level Understanding Data. We include LLaVA-OneVision (Li et al. 2024a
Open-LLaVV--Next (Chen & Xing 2024), MAmmoth-VL, (Guo et al. 2024). and Visual- WebInstruct (Jia et al. (025)..
C: Region-level Understanding Data. We include GranD Rasheed al. 2024) and Ref- COCO (Kazemzadeh et al.) 2014).
D: Image Editing Data. We include ShareGPT4o-Image (Chen et al. 2025b). GPT-Edit- 1.5M (Wang et al. 2025b), and the image editing subset of UniWorid-Vi (Hu et al. 2022).
E: Interleaved Planning and Reflection Data. For planning data, we manually construct a layout dataset by running an open- open-vocabulary object detector, GroundingDino-L (Liu al.] 2024b), on the outputs of image generation and editing datasets, including BI.I--0 (Chen et al. 2025a). ShareGPP4oo-Imaal
(Chen al,, (025b), and GPT-Edit- 1.5M (Wang et al., b025b). For reflection data, we leverage existing datasets including ReflectDiT (Li et al.
2025b) and ReflectionFlow (Zhuo et al. 2025).
Training Setup. Training consists of three stages. In the first stage, we extend LaViDa to region- level tasks such as grounding. In the second stage, we perform large-scale pretraining on text-to- image generation tasks. In the final stage, we jointly train the model on mix of understanding, generation, and interleaved tasks. We document the training hyperparameters, the datasets used, the active parameter count, and other relevant details in Table
In addition, we implement a dataset mix scheduler that dynamically adjusts the sampling weight of each dataset throughout training to address data imbalance. Specifically, we assign high weight to new capabilities at the beginning of each training stage and gradually decay the weight over time. For example, in Stage we have fewer than 1M grounding samples but more than 10M image-level understanding samples. To enable efficient acquisition of grounding capability while preventing overfitting, we initially set the grounding- tonoiingtoouundrstaadnnnnn ratio to 3:1, which is gradually decreased to 1:3, We provide further analysis of the scheduler in Section B.4
B.2 ABLATION STUDIES ON ELASTIC-MON MOT DESIGN
In this section, we report ablation results of the Elastic-MoT design, including the size of the gener- ation branch and the number of joint attention layers.
Size of Generation Branch. We report the performance of Lavida-O with different sizes of the generation branch during text-to-image pretraining (Stage 2) in Table g The results are obtained after 50k training steps with a global batch size of 1024. We also document the maximum per-GPU batch size, the gradient accumulation steps, and the training latency to measure efficiency. The results show that models of different sizes achieve comparable performance after 50k steps. Smaller models (IB, 2B) converge slightly faster and achieve marginally higher performance than larger models (4B, 8B). Larger models (4B, 8B) are harder to optimize as they need more steps, data, and tuning to realize their full capacity. In terms of latency, smaller models considerably faster. The 2B model achieves the best balance between performance and efficiency, attaining the highest GenEval and DPG scores while being 3. 3. faster.
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Table 7: Training configurations across three stages. We use letters A-E represent different
dataset following Section B.1
Stage 1
Stage 2
Stage 3
Learning Rate
5 10-6
10-4
2 10
Steps
80k
400k
100k
B1
0.99
0.99
0.99
B2 optimizer
0.999
0.999
0.999
AdamW
AdamW
AdamW
Dataset Used
B,C
A 6.4B
A.B.C,D.E
Loaded Parameters
8B
10.4B
Trainable Parameters
8B
2.4B
10.4B
Und. resolution
384 (1,
384 x (),,),, (2
384 x ((1,3), (2,
Gen. resolution
256 + 512 - 1024
1024
Semantic Encoder
Trainable
Not Loaded Loaded Trainable
Trainable
VQ Encoder
Not Loaded Not Loaded
Loaded
Gen. Branch
Trainable
Und. Branch
Trainable
Partially Loaded
Trainable
Table 8: Comparison of different model sizes GenEval, DPG, and training efficiency. We report the performance of Lavida-O with different sizes of the generation branch during the text- to-image pretraining (Stage-2) after 50k training steps. We also report the per-GPU batch size and training latency.
Architecture
Performance
Efficiency
Parm.
Hidden Size
GenEval f
DPG f
Batch Size
Accum. Step
Latency (s/it)..
4B+IB
1536
0,56
60.8
16
1.98
4B+2B
2048
0.57
63.1
3.67
4B+4B
3072
0.48
55.3
8
2
8.42
4B+8B
4096
0.55
58.6
11.64
Number Joint Attention Layers. To study the effect of varying the number of joint attention layers, conducted two ablation experiments. The first experiment was performed during Stage pretraining. We started with the Stage checkpoint with = 32 layers in the understanding branch and fixed the generation branch size to 4B. We then varied M, the number of layers with joint attention, among (8, 18,16,2,32). The number of non-joint layers, K, is automatically determined by K N M. The results after 100k training steps are shown in Table Among the four choices, .M = {16, 24, 32) yields a comparable performance, while M = 8 shows a substantial drop. This suggests that a sufficient number of joint attention layers is necessary for strong text-to- image performance, but additional layers beyond a threshold provide little benefit. Training latency also decreases when M smaller (i.e., larger K), fewer joint layers must be loaded. M 16 achieves the best balance of speed and performance.
Table Effect of varying choices of M and K in partially-decouple attention design. Efficiency is measured in Stage-2 training. For stage-3 training, we need to load all layers since the data contain a mix of text, image, and interleaved generation tasks.
M K
Pretraining (Stage-2)
SFT (Stage-3)
Efficiency
GenEval f DPG f
GenEval f DPGt
ImageEditt
Latency (s/it).
8
24
0.57
69.3
2.45
16
16
0.63
75.0
0.89
83.2
3.66
3.67
24
8
0.63
73.3
0.81
83.0
3.60
4.12
32
0
0.61
71.2
0.85
83.2
3.55
5.20
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Validation Loss during T2I Training
8.4
Scratch Truncated
8.0
Loss
7.8
7.4
2500
5000
7500
10000
12500
15000
17500
20000
Training Step
Figure 12: Effect of truncated initialization. Validation loss comparison of truncated initialization vs. training from scratch during Stage 2. Truncated initialization converges faster and achieves lower loss.
We conducted second experiment in Stage 3, where interleaved generation and editing tasks may benefit more from joint attention. Starting from a Stage checkpoint pretrained with layers for 400k steps, we trained for 50k steps under M {16, 24, 32). The results show two key observations: (1) eoxt-to-image tasks converge faster than image-editing
16
tasks, reaching near-final performance after 50k steps, while editing tasks lag behind; (2) increasing M does not significantly improve performance, even for interleaved editing. This may be due to token interference at later layers the Stage 2 model being optimized with only 16 joint layers. Due compute constraints, we were unable to retrain Stage 2 with alternative values of M. Nevertheless, keeping M = 16 is reasonable choice given our setup. Finally, Stage the efficiency difference less pronounced, since all 10.4B parameters must be loaded for interleaved training and inference.
Weight Initialization. We initialized the 2.4B generation branch with truncated weights from the understanding branch (Section A.2). We also explored initializing from scratch. Figure T2 shows the validation loss during the first 20k steps of Stage Truncated initialization converges faster and yields lower loss.
B.3 ABLATION STUDIES ON STRATIFIED SAMPLING
We compared image generation quality under different sampling strategies on the MJHQ-30K dataset (Li et al. 2024b) with 64 sampling steps. We evaluate the proposed stratified sampler against confidence- baaiidence-based sampling (Chang et al.) (022), uniform random sampling, and Halton sampling (Besnier al. 2025). The results reported Table I0 The stratified sampler achieves the best performance.
Table 10: Performance Different Samplers Text-to-Image Generation Tasks. We report the FID scores on MJHQ-30K dataset using different samplers. The proposed stratified sampler achieves the best outcome.
Method
FID-30k +
Confidence
11.42
Uniform
8.22
Halton
7.38
Stratified
6.68
B.4 ABLATION STUDIES ON DATA PIPELINE
Effect of Task Scheduler. To study the effect the dataset scheduler described in Section B.I compare three dataset mixing strategies in Stage training. The goal of Stage is equip
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VaViDa with region-level understanding capabilities such as grounding. At this stage, training data includes fewer than 1M grounding samples but over 10M image-level understanding samples. To mitigate imbalance, employ scheduler that dynamically adjusts the sampling weights for new (grounding) and existing (image-level) capabilities. Fach batch is drawn from a single dataset. For example, when New:Old=1:3, average of batches contain grounding data and contain image- level data.
We initialize the ratio New:Old-3:1 and gradually reduce it 1:3, We compare against fixed ratios of 1:3 and 3:1, reporting results after 20k steps in Table[]] Fixing New:Old=1:3 under-trains grounding. while fixing New:Old=3:1 improves grounding but causes forgetting on image-level un- derstanding. In contrast, the dynamic scheduler achieves strong performance on both. Notably, even outperforms the fixed 3:1 setup on image-level understanding, suggesting also mitigates overfitting caused by the small grounding dataset.
Table 11: Comparison of different task scheduling during Stage 1 Training, We compare the performance under different dataset sampling weights of new capabilities (grounding) and old capa- bilities (image-level understanding). We explored two fixed sampling ratio 1:3 and 3:1 for New:Old. For the dynamic scheduler, the New:Old ratio is initialized as 3:1 and gradually decreased to 1:3,
Method
New Capabilities
Existing Capabilities
RefCOCO
RefCOCO+
RefCOCOg
MME
CharlQA
ScienceQA
New:Old = 1:3
83.2
74.6
78.3
449
72.6
84.3
New:Old 3:1
88.8
82.4
85.7
349
65.0
75.8
Dynamic
92.0
86.9
89.3
436
73.4
86.4
Does understanding data help generation tasks? To examine whether incorporating understand- ing data benefits generation, we experimented with removing all grounding data from Stage 3. The results are shown in Table 12 Even without explicit planning, incorporating grounding data en- hances both text-to-image generation and editing, highlighting an inherent synergy between the tasks. When planning is enabled, these benefits compound, leading to even greater improvements.
Table 12: Effect of Grounding Data in Stage 3 Training. To analyze the impact of the synergy between understanding and generation tasks, we explored removing object grounding in Stage Training. This leads to worse overall performance. This demonstrates that jointly training on both understanding (grounding) and generation tasks is helpful for generation.
Method
GenEval
DPG
ImgEdit
w/o grounding data
0.74
82.0
3.60
w/ grounding data
0.77
81.8
3.71
planning 0.85
82.9
3.80
B.5 ABLATION STUDIES ON REFLECTION AND PLANNING
Breakdown of Performance Improvements. We provide detailed breakdown of the gains in- troduced by planning and reflection. Table [3 shows results on GenEval. Planning yields large improvements in object positioning (+0.19), while reflection additionally improves counting and tribution. To further examine the behavior of planning, we conducted additional text-to-image eval- uations on categories of T21-Comphench++ (Huang et al. (025) that are not covered by GenEval benchmark, including 3D spatial constraints and object texture attribution. We report these results in Table [4 We observe that Lavida-O consistently demonstrate strong performance, with planning mechanism offering significant performance boost. Notably, while our planning process use only 2D bounding boxes, we observe that it also improves satisfaction of 3D positional constraints by properly designing the size of relevant objects to reflect the distance.
On Image-Edit (Table 15). planning improves adding/removing objects, subject actions, and hybrid instructions. The largest gains are in action (+0.50) and hybrid (+0.16). However, global edits (e.g..
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style, background) degrade slightly, these tasks less aligned with grounding. A promising direction for future is to let the model dynamically decide whether to invoke planning.
Table 13: Breakdown of performance improvements on GenEval Dataset. We report the im- provements of the planning and reflection mechanism each category of the texi-to-image gener- ation tasks from GenEval Dataset.
Single
Two
Position
Counting
Color
Attribution
Overall
Baseline
0.99
0.85
0.65
0.71
0.86
0.58
0.77
+Planning
0.99
0.94
0.84
0.75
0.90
0.68
0.85
vs. Baseline
+0.09
+0.19
+0.04
+0.04
+0.10
+0.08
+Reflection 1.00
0.95
0.89
0.85
0.90
0.74
0.89
A vs. Baseline
+0.01
+0.10
+0.24
+0.14
+0.04
+0.16
+0.12
Table 14: Additional Text-to- -eeeett-ennratiion results on 121-Comphench-44 Benchmark. We report results on categories included in GenEval benchmark, such as 3D spatial constraints and texture attribution.
Model
3D
2D
Texture
Stable Diffusion 2 (Stability Al 2022)
0.323
0.134
0.492
AanusPro-7BB(Cheee al. 2025e
0.323
0.157
0.407
FLUX.1 Dev( 2024
0.387
0.286
0.692
LaViDa-O
0.414
0.388
0.613
+Planning
0.442
0.390
0.715
Table 15: Breakdown of performance improvements on Image-Edit Dataset. improvements of the planning mechanism on each category of the image editing tasks from Image- Edit Dataset.
We report the
Model
Add
Adjust
Extract
Replace
Remove
Background
Style
Hybrid
Action
Overall
Baseline
4.04
3.62
2.01
3.98
4.06
4.82
2.94
3.54
3.71
Planning
4.11
4.05
4.75
3.80
Baseline
+0.07
+0.05
+0.03
+0.01
+0.07
-0.06
-0.07
+0.16
+0.50
+0.09
Table 16: Performance and Latency at different numbers of reflection rounds N. When N = 1, only perform planning.
Num. of Reflection Rrounds
N=1
N=2
N=4
N=8
N=12
N=16
N=20
GenEval Score
0.848
0.864
0.875
0.882
0.890
0.886
0.886
Latency (s/image)
27.2
32.6
39.3
47.1
53.4
58.3
62.2
Effeet of Inference-time Scaling. We evaluate reflection scaling by varying N, the maximum number of images generated per prompt. Table 16 shows results. Even one reflection step (N 2) improves performance. Gains saturate at N 8, with little benefit beyond. Latency grows sublinearly with since simple prompts often trigger early stopping. For example, when = 20, the model may obtain satisfactory output and terminate the generation process after generating just two images.
B.6 ABLATION STUDIES ON UNIVERSAL TEXT CONDITIONING.
To examine the effectiveness of universal conditioning, we perform a user study and ask human evaluators compare image generation with and without universal text conditioning. We curated
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Humen Evaluation Results
60
Ratio (%)
20
w/ Univ. Conditioning
Tie
w/o Univ. Conditioning
Figure 13: Human Evaluation of Image Quality. We conduct study on image quality and compare text-to-image generations with and without universal text conditioning. Results show that universal text conditioning with aesthetic scores greatly improves image quality.
total of 300 human response on image pairs generated with randomly selected prompts from MJHQ- 30k dataset. The human evaluators are provided with the following instruction:
Instructions
Both of these images were generated by Al models trained create image from text prompt. Which image do you prefer given the associated text? Example criteria could include: detail, art quality, aesthetics, how well the text prompt is reflected, lack distortions/irrggllarttss (e.g. extra limbs, objects). general, choose which image you think you would consider to be "better".
Results show that human evaluators exhibit strong preference towards images generated with universal text conditioning, suggesting that conditioning the image generation with quality scores through our proposed universal text conditions method can effectively improve image quality.
We report the results Figure 13
B.7 SPEED-QUALIE TRADEOFF
A key advantage of masked diffusion models autoregressive models is the speed-quality trade- off enabled by parallel decoding. We study this in the unified setting by evaluating Lavida-O on MJHQ-30K text-to-image generation (Li et al. (2024b), RefCOCO grounding (Kazemzadeh et al., 2014). and Math Vista reasoning (Lu et al. 2023).
For MJHQ and RefCOCO, we vary the number of diffusion steps. For Math Vista, we employ Fast- DLLM (Wu et al.) 2025a). which adaptively unmasks multiple tokens per step. The tradeoff is controlled via its threshold hyperparameter. Results are shown in Figure [4] For MJHQ we report
FID (lower is better), for RefCOCO Dacision@0..3 (higher is better), and for Math Vista accuracy (higher better).
We compare against several baselines: Flux (Labs) 2024) on T2I, Qwen2.5-Vi VL.7BB (Bai et al. 2025) on grounding, and Swen2.5-vLL/Open-LLLa VA---LLLLVVA----------- (Chen Xing 2024) on reasoning. Lavida- O achieves faster inference and stronger quality image generation and grounding. For grounding, it reaches up to 6.8x speedup while surpassing Qwen2.5- 5-VL-7B in precision. On Math Vista, while less accurate than state-of-the--s AR models, Lavida-O is much faster, and still stronger than pop- ular AR baselines such as Open-LLaVA, Leaa-LLVV-Next-8BB Performance also exceeds the base La LiViDa (56.9 44.8).
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MJHQ FID
RefCOCO P@O.5
MathVista Acc
11
Fux.1-dev-126 LaViDa-O
Dwe.2.55V1-72 LaViDa-O
Open-LLaVA-Next-BB
92.0
Owen2.55V1-7
10
LaViDa-O
91.5
9
55-
91.0
8-
90.5
7-
90.0
10
15
20
25
30
35
0.25
0.50
0.75
1.00
1.25 1.50
1.75
10
12
14
Latency (s/sample)
Latency (s/sample)
Latency (s/sample)
Figure 14: Speed-quality tradeoff generation, grounding, and reasoning, Latency (s/sample) and benchmark scores are shown. For MJHQ: FID (lower is better). For RefCOCO: Precision@Us 0.5 (higher is better). For Math Vista: accuracy (higher is better). On Math Vista, the maximum genera- tion length is capped at 256 tokens.
B.8 ADDITIONAL QUALITATIVE RESULTS
Finally, provide additional qualitative examples demonstrating Lavida-O's capabilities on diverse prompts and editing instructions. Figure [5 shows text-to-image generation, and Figure [6 shows image editing results.
C COMPUTE COST
All experiments conducted 8 nodes, each equipped with 8 A100 GPUs. The total training amounts to 34.2 days measured by wall clock time, or 53k GPU hours.
D LIMITATIONS
In this section, we discuss several limitations of Lavida-O.
Text Rendering. Since the image generation branch is trained from scratch and we did explicitly include datasets for text rendering, Lavida-O 's capability to render and edit text is very limited. We also find that the VQ image tokenizer address this issue in future work by incorporating additional text rendering data and finetune the VQ image tokenizer on screenshots of documents.
cannot faithfully reconstruct small texts. We aim
Pixel Shift. Our image editing datasets, such as GPT-Image-Edit. Edit--.d.i-MMM (Wang et al. 2025b contains images distilled from generative models like GPT-4o, which known to have "pixel shift" problems. Specifically, even if the instruction only requires editing specific region, the other regions may still experience small but noticeable changes. As a consequence, Lavida-O inherit this problem. We aim to mitigate this by obtaining more clean and high-quality image-editing data.
Math Reasoning. The focus of Lavida-O to build a unified multi-modal MDMs capable both understanding and generation tasks. Although its math reasoning capabilities has improved from the base model VaViDa thanks to additional training, there remains a considerable gap when compared against state-of-the-ar models. We leave further improvements math reasoning tasks future work.
Hallucination. Like all generative models, may occasionally produce inaccurate fabricated information. We recommend using model outputs as guidance rather than unquestioned truth, and validating them where accuracy is critical.
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E BOARDER IMPACT
Lavida-O has strong text-to-image generation capabilities and image-editing capabilities, which may be abused to create various harmful and offensive content. We strongly caution the community against such use cases. Additonally, our model may inherit the biases embedded in the base model VaDiDa, as well as biases incorporated in the images and texts of the training data. Our model is intended to be used by researchers to build strong diffusion model for multi-modal applications and explore methods of building future multi-modal foundational models. We do not recommend that it be used for any other purposes.
F
LLM USAGE
We use LLM to correct typos and grammatical errors only.
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lush garden the middle of dimly It library. fantasy, realistic, 4k
A portrail of young with striking green eyes and freckles, wearing flowing green windy
Close-up kitlen with playful wicker basket in background, ultra HD
cule winter kawaii, Pixar, ultra detailed. glacial backiground, extremely realistic
up close swamp monster, big scery smile, sea weed on the head, dark bog with fog
2023 land rover defender black, camping the forrest, realistis, foggy morning
close-up portrail of an elderly African man with wise expression, wearing traditional Kente cloth, ultra photorealistic.
artificial intelligence fulure, robot, humanoid, realistic, detailed, dramatic lighting. hyper realistic
Magical forest, flower meadow, golden light shining through the fress, castle in the distance, photo realistic
beautiful hibiscus flower, Bk. realistic, octane render, artstation, cinematic, ultra hd
A handsome queen fairy godess with green eyes and chariematic face, offering water of life, lush background,
spicy food, ultra realistic style. With cinematic lighting, closeup shot, 75 mm lens, production quality, depth
little sunglesses and colorful sparides, style of made of wire, solarpunk, daireobscure lighting
fallen angel, with wings engulfed flames, kneels despair as embers flicker
Beautiful woman with perfect face long black hair, woman wearing ancient Chinese dress her hair, woman is standing mythical Tiamat dragon
besutiful lttlle girl, big soutful eyes, sweet smile, 8k. anime, background hdr, portrait, closeup portrail. violel eyes hair. image violet and turquoise tones
Figure 15: Qualitative examples of text-to-image generation. We provide additional examples of text-to-image generation outputs on diverse prompts.
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Replace with a sofa.
Make the person in the image smile.
Construct the elephant bricks.
remove the umbrella
Transform the donut's aluminum
Convert ink wash painting
colorize this photo high definition.
Remove all the people.
Change the hair of the person photo yellow.
style.
change the color jacket purple
Replace the bird in the image with small rebbit.
Figure 16: Qualitative examples of image editing. We provide additional examples of image editing outputs on diverse instructions.
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Reviews:
[{"review_id": "WUykL3jwuZ", "reviewer": "Reviewer_GnXG", "summary": "This paper presents a unified masked model that is able to tackle multimodal understanding and generation. Specifically, several components, including Elastic Mixture-of-Transformers, planning, and iterative self-reflection, were proposed. Extensive experimental results on understanding and generation benchmarks demonstrated the effectiveness of the proposed approach.", "questions": "See weaknesses. I would adjust my initial rating according to the authors' response.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper is well-organized and easy to read.  \n2. The proposed approach demonstrates promising results on several understanding and generation tasks, such as grounding and editing.  \n3. Comprehensive evaluations are conducted.", "weaknesses": "1. The proposed masked diffusion language model is not relatively interesting, which was explored in UMDD[1].  \n2. The overall methodologies, including mixture-of-transformer, discrete language model, and discrete diffusion, are well-studied by the existing works [1,2,3,4,5]. I cannot find a significant contribution of this work.  \n3. There are several recent works with models of around 2B parameters, which should be fairly compared in the benchmarks.\n\n\n[1] Unified Multimodal Discrete Diffusion.\n[2] LMFusion: Adapting Pretrained Language Models for Multimodal Generation.\n[3] Emerging Properties in Unified Multimodal Pretraining.\n[4] MMaDA: Multimodal Large Diffusion Language Models.\n[5] Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "A6hoFoFJ62", "reviewer": "Reviewer_NaEt", "summary": "The paper proposes LaViDa-O, a unified masked diffusion model that brings image understanding, object grounding, text-to-image generation, image editing, planning and self-reflection into one architecture. It includes several techniques, like Elastic-MoT, modality-aware masking, universal text conditioning, and stratified sampling. Empirically, the planning and reflection produces noticeable gains on text-to-image and editing benchmarks, and the model also improves understanding relative to the LaViDa base.", "questions": "1. For editing images, it may be the case that other conditioning modalities (e.g., keypoints, masks) might be more beneficial than bounding boxes. Is there a way to extend the grounding mechanism to such modalities?\n\n2. The paper states that bounding boxes are denoised in a single step. Could the authors clarify how this is implemented (when to unmask)? Also, is the timestep necessarily before the denoising step of an [exp] token?\n\n3. During training, do image tokens follow a separate masking schedule until $t_{\\text{exp}}$? If so, would unmasking an [exp] token before or after $t_{\\text{exp}}$ introduce a train-inference mismatch?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper is clearly written and well organized.\n\n2. The method combines multiple ideas designed for different challenges (e.g., stratified sampling). There is a clear motivation before proposing each.\n\n3. Elastic-MoT is a sensible design for improving efficiency, and the appendix provides informative ablations exploring its effectiveness.\n\n4. The single model shows clear performance gains across multiple dimensions (e.g., generation and understanding), particularly when incorporating planning and reflection.\n\nOverall, the paper introduces several clever design choices, such as modality-aware masking and stratified sampling, each of which is well-motivated and justified. So, I believe this work will positively influence the development of masked diffusion models.", "weaknesses": "1. Reporting results on some components of T2I-CompBench++ would strengthen the evaluation, especially since some of its focused prompts (e.g., for 3D and texture) are not currently covered by GenEval.\n\n2. The contribution of universal text conditioning is not quantitatively assessed. A user study or automated evaluation demonstrating its benefit would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "g8SOKBSweP", "reviewer": "Reviewer_VkdP", "summary": "This paper introduces Lavida-O, a unified Masked Diffusion Model (MDM) designed to handle a wide range of multimodal tasks, including image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. The key idea is to build a single, efficient framework that surpasses existing specialized models and other unified approaches (like AR or AR+diffusion models).", "questions": "- The planning and reflection mechanisms appear to be invoked via specialized prompts (e.g., \"please generate a layout...\", Sec A.7). Does the model have any capability to *autonomously* decide when to use planning or reflection based on its own assessment of the prompt's complexity? Or must the user always explicitly invoke these modes?\n- Regarding the \"pixel shift\" limitation: Beyond identifying the training data as the source, have the authors explored any mitigation strategies? For example, could a mask-preservation loss (e.g., L1 loss on non-edited regions) be applied during the image editing finetuning stage?\n- The modality-aware masking (Sec 3.1.2) is shown for single-turn planning/reflection. How robust is this mechanism for more complex, multi-turn interleaved tasks (e.g., generating a story with multiple images and text blocks)? What are the practical context length limits when history includes multiple VQ-tokenized images?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The proposed architecture is efficient and effective. It intelligently combines a large 8B understanding model with a smaller 2.4B generation model, improving training/inference efficiency (Fig 5) while maximizing performance by leveraging the pre-trained understanding branch.\n\n- The integration of planning (layout generation) and self-reflection (iterative refinement) is a key innovation. It provides a concrete mechanism for the model's understanding and generation capabilities to mutually benefit each other, leading to large, quantifiable gains in prompt-following and editing (Tables 3, 13, 14).\n\n- The paper is packed with valuable technical innovations for MDMs, including stratified random sampling (improves FID, Table 10), universal text conditioning (improves control), and coordinate quantization (enables parallel decoding for grounding).", "weaknesses": "- The paper acknowledges that the model's text rendering capability is \"very limited.\" This is a significant weakness for a model aiming for general-purpose multimodal capabilities, as text is a common element in user requests. The authors attribute this to the VQ tokenizer and data, but it remains a key area for improvement.\n- The model suffers from a \"pixel shift\" problem, where non-edited regions are altered during editing. While attributed to the training data, this lack of precision detracts from the quality of the editing feature.\n- While performance on MathVista improved over the base model, it still \"lags behind state-of-the-art AR models\" (Sec D, Fig 13). This indicates that the MDM architecture, or at least this implementation, has not yet closed the gap on high-level, abstract reasoning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NDpjf6zO8k", "reviewer": "Reviewer_dKrA", "summary": "This paper presents Lavida-O, a unified Masked Diffusion Model for multimodal understanding and generation. Building upon LaViDa, the model integrates an Elastic Mixture-of-Transformers (Elastic-MoT) architecture with asymmetric branches for efficient parameter sharing. It supports diverse tasks such as image understanding, object grounding, image editing, and high-resolution text-to-image generation, achieving state-of-the-art performance on multiple benchmarks.", "questions": "see the weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.Proposes a unified framework that effectively bridges image understanding and generation.\n\n2.The Elastic-MoT architecture is efficient, reducing training cost while maintaining strong performance.e\n\n3. Demonstrates impressive empirical results across several benchmarks, indicating strong generalization.", "weaknesses": "1.The novelty of the work appears somewhat incremental — the Mixture-of-Transformers (MoT) paradigm has been extensively studied, and the adoption of discrete diffusion is also not particularly new. The paper’s main contribution seems to lie in engineering integration and architectural refinement, rather than proposing a fundamentally new concept.\n\n2.The training cost appears rather high. According to the appendix, the model requires around 34 days of training, which may be excessive for a unified framework. It would be helpful if the authors could discuss potential efficiency improvements or training optimizations that make the approach more practical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)