UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing
Authors: Hao Tang
Chen-Wei Xie
Xiaoyi Bao
Tingyu Weng
Pandeng Li
Yun Zheng
Liwei Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Multimodal Understanding and Generation
Image Editing
CLIP
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: In this paper, we propose UniLIP, a unified framework that adapts CLIP for multimodal understanding, generation and editing. Although CLIP excels at understanding, it lacks reconstruction abilities required to be a unified visual encoder. However, previous CLIP-based unified methods fail to balance understanding and reconstruction, leading to semantic degradation or inconsistent reconstructions. In contrast, we introduce a novel two-stage training scheme with a self-distillation strategy that progressively endows CLIP with high-fidelity reconstruction abilities while preserving its original comprehension performance. For enhanced reasoning and consistency in generation and editing, we further develop a dual-condition architecture built upon the MetaQuery framework. Our architecture jointly utilizes multimodal hidden states for rich contextual details and learnable query embeddings to harness the powerful reasoning abilities of Multimodal Large Language Models (MLLMs). Leveraging advanced image representation and architectural design, UniLIP demonstrates superior instruction following and edit fidelity. With only 1B and 3B parameters, UniLIP can outperform larger unified models such as BAGEL (7B) and Uniworld-V1 (12B), achieving state-of-the-art performance of **0.90** on GenEval, **0.63** on WISE, and **3.94** on ImgEdit. These results demonstrate that UniLIP successfully expands the application of CLIP, establishing its continuous features to not only serve as the optimal choice for understanding tasks but also achieve highly competitive performance in generation and editing tasks. Code and models are available at https://github.com/nnnth/UniLIP.

Published as a conference paper at ICLR 2026
UNILIP: ADAPTING CLIP FOR UNIFIED MULTIMODAL UNDERSTANDING, GENERATION AND EDITING
Hao Tang".3 Chenwei Xie", Xiaoyi 2
Tingyu Weng",
Pandeng Li
Yun Zheng". Liwei Wa.5..4.5
CCette for Data Science, Peking University "Alibaba Group
3 "CASIA Center for Machine Learning Research, Peking University
5 State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China (tanghacêstu, wanglw8eis] pku. edu. baoxlaoy12021eia ac.
(eniac. xcw, wengtingyu.wtt wty, lipandeng. zhengyun, sygouun.yyyy. 0 ibaba waabbaaaainnn iinaaain.ccoo
ABSTRACT
In this paper, we propose UniLIP, a unified framework that adapts CLIP for mul- timodal understanding, generation and editing. Although CLIP excels at under- standing, it lacks reconstruction abilities required to be unified visual encoder. However, previous CLIP-based unified methods fail to balance understanding and reconstruction, leading semantic degradation inconsistent reconstructions. In contrast, we introduce a novel two-stage training scheme with a self-distillation strategy that progressively endows CLIP with high-fidelity reconstruction abilities while preserving its original comprehension performance. For enhanced reason- ing and consistency in generation and editing, we further develop dual-condition architecture built upon the MetaQuery framework. Our architecture jointly utilizes multimodal hidden states for rich contextual details and learnable query embed- dings to harness the powerful reasoning abilities of Multimodal Large Language Models (MLLMs). Leveraging advanced image representation and architectural design, UniLIP demonstrates superior instruction following and edit fidelity. With only 1B and 3B parameters, UniLIP can outperform larger unified models such BAGEL (7B) and Uniworld-V V1 (12B), achieving state-of- -hhe------h-rrtttt performance 0.90 on GenEval, 0.63 on WISE, and 3.94 on ImgEdit. These results demon- strate that UniLIP successfully expands the application of CLIP, establishing its continuous features to not only serve as the optimal choice for understanding tasks but also achieve highly competitive performance in generation and editing tasks. Code and models are available at https: / //gitttin gighub..... com/ nnnth/////////nnm/ UIiLIP.
INTRODUCTION
The success of Large language models (LLMss) has driven unified modeling into multimodal do- mains, particularly image understanding and generation. However, traditional approaches for these two tasks differ significantly: image understanding models align CLIP-like (Radford al., 2021) semantic encoders with LLMs (Bai et al., 2025; Zhu et al., 2025), while image generation models either employ diffusion model VAE latents (Xie al., 2024a; Esser et al., 2024) or apply autoregressive modeling on discrete tokens encoded by VQVAE (Van Den Oord et al., 2017; Sun al., 2024a). This divergence has sparked significant interest in a more unified approach.
Early efforts (Team, 2024; Zhou et al., 2024) combine VQVAE or VAE with LLMs for unification, but often achieve subpar performance in understanding because the encoder struggles to capture rich semantics. To mitigate this, recent works build unified tokenizers based on CLIP (Wu et al., 2024b; Sun et al., 2024b; Qu et al., 2024; Han et al., 2025). While CLIP features possess rich semantics, they lack pixel details, requiring extra modifications for reconstruction. As depicted in Figure (a), al., 2024b; Qu et al., 2024) discretize CLIP features, at the cost of information
VILA-U et al. (Wu at. loss and worse understanding than the original CLIP. Emu2 (Sun et al., 2024b) freezes CLIP and finetunes a diffusion decoder to reconstruct images from CLIP features. However, as CLIP features lose pixel details, the diffusion decoder cannot produce consistent outputs (example in Figure 1),

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VQ Decoder
A
Pixel Decoder
Pixel Decoder
Condition
Projection
Quantizer A
DIT
+
Distillation
Original Image
(6) Quantization
PSSR-21.22
CLIP
CLIP
CLIP
CLIP
Noise
degrodation
preservation
semontic preservation
V good reconstruction
X inconsistent reconstruction
/ better reconstruction
(a) Quantization
(b) Diffasion Decoder
(c) Ours: UniLII
Diffasion PSNR-11.17
Oct: UniLiP PSNR 24.93
(VILA-U al.)
(Emu2 al.)
Figure I: CLIP-based reconstruction methods. (a) Quantizing CLIP features into discrete tokens. (b) Using CLIP features as the condition for the diffusion transformer. (c) Ours: Training CLIP for reconstruction with self-distillation constraints. The right part shows comparison of reconstruction results. The diffusion decoder produces incorrect hole positions and counts, while quantization- based methods exhibit distortions around the holes. In contrast, UniLIP achieves the best quality.
hindering editing tasks. Therefore, these methods either compromise CLIP's original understanding performance or achieve inconsistent reconstruction, leading to a trade-off in unification.
To build optimal unified tokenizer based CLIP, we identify two key challenges: (1) How to adapt CLIP for reconstruction without compromising its original comprehension capabilities?
Training CLIP solely for reconstruction will lead to catastrophic forgetting in comprehension. (2) How to effectively utilize CLIP in generation and editing? While BI.IP--0 (Chen et al., 2025a) val- idates CLIP is better generation representation than VAE, it follows the reconstruction method of Emu2 (Sun et al., 2024b), requiring an extra diffusion process to decode CLIP features into images. This design not only adds computational costs but also hinders editing that demands consistency. UniWorld-VI (Lin et al., 2025a) supports editing by conditioning on SigLIP (Zhai et al., 2023) fea- tures, but limited to high-resolution images and depends on VAE features for generation, which are misaligned with the SigLIP features. Therefore, more effective approach is required to enhance CLIP's reconstruction and establish it as a bridge between understanding and generation tasks.
In this paper, we propose UniLIP, adapting CLIP for reconstruction, generation and editing without losing original understanding capabilities. For reconstruction, we propose a two-stage training ap- proach: the first stage trains only the pixel decoder to align with the frozen CLIP, and the second stage enables CLIP for training. To further restrict feature distribution changes, as shown in Figure (c), adopt self-distillation loss in the second stage, using the frozen CLIP the teacher. To enable generation with UniLIP features, we follow the architecture of MetaQuery (Pan et al., 2025). However, this approach is ineffective for editing since fixed-length queries fail to capture rich in- formation in visual features, leading to inconsistencies. Therefore, we propose a dual-condition architecture: both the multimodal hidden states the MLLM and the query embedding are used as conditions for diffusion. This approach not only fully utilizes the information in UniLIP features, but also leverages the reasoning capabilities of the MLLM through the query embedding.
We implement UniLIP based on Intern VIT from Intern VL3 (Zhu et al., 2025). After our two-stage training. UniLIP achieves the best reconstruction performance among CLIP-based methods while preserving understanding performance. Surprisingly. UniLIP also achieves improvements on several comprehension benchmarks (see Table 1), likely because reconstruction training allows UniL.P to capture more image details (see Figure 2). For generation and editing, train with 40M public images. As our unified representations are well-aligned with text and reconstructable, UniLIP can achieve superior prompt alignment and edit consistency. In image generation, UniLIP outperforms all similarly-sized unified models on the GenEval and WISE benchmarks, scoring 0.90 and 0.63 respectively. In image editing, UniLIP obtains larger models like UniWorld-V1 (Lin et al., 2025a) and BAGEL (Deng et al., 2025).
of 3.94 ImgEdit, significantly surpassing
In summary, main contributions are listed follows:
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Published as a conference paper at ICLR 2026
(1) We propose UniLIP, which inherits CLIP's ofate-of-the-rt understanding capabilities and ex- tends its application to generative tasks, achieving strong performance on multiple benchmarks.
(2) We introduce novel training approach that extends CLIP for image reconstruction, overcoming its deficiency in capturing pixel details while preserving its original comprehension abilitics.
(3) We propose dual-condition architecture to bridge MLLM and the diffusion transformer, which effectively avoids information loss and maximizes UniLIP's potential in editing tasks.
Reconstruction PSNRT
Understanding MMVP
Model
rFID
SSIM
MME-P1
MMBenchh
A12D1
TextVQAt
Frozen CLIP (Zhu al., 2025)
6.14
16.26
0.572
1492
72.6
67.3
69.4
74.1
UniLIP
0.31
24.62
0.788
1499
72.6
68.7
70.7
74.7
Table Reconstruction and understanding performance comparisons. "Frozen CLIP" refers to the Intern ViT, and we train pixel decoder reconstruct images from its features.
Are there only two cakes in this image?
Which label represents Crust?
F
F
H
H
Original Image
Frozen CLIP: Yes X
UniLIP: No
Original Image
Frozen CLIP: A X UniLIP: B
Figure 2: Qualitative comparison of reconstruction and understanding results. In the first ex- ample, the reconstructed image from frozen CLIP only shows two cakes, and the cake on the left edge is indistinguishable. In the second example, the letters are not visible for frozen CLIP. This lack of detail results misunderstanding, while UniL.IP can capture them and answer correctly.
2 RELATED WORK
MILMs for Image Understanding. Initial approaches (Liu et al., 2023; Zhu al., 2023; Dai al., 2023) align the features of visual encoders to LLM input spaces, achieving strong performance through instruction tuning. In this architecture, the visual encoder is crucial for perception, and CLIP (Radford et al., 2021) has become the optimal choice due to its robust alignment with text. Subsequent methods have explored the use of better CLIP, including SigI.IP (Zhai et al., 2023) and Intern VIT (Chen et al., 2024d), enhancing performance through improved designs (Shi et al., 2024: Tong et al., 2024a), larger model sizes (Liu et al., 2024a), better training strategies (Deitke et al., 2024), and extension fine-grained perception tasks (Wang et al., 2024b; Tang et al., 2025). Nevertheless, these models remain specialized in comprehension without generative capabilities.
Unified Visual Tokenizers. Early works VQ/VAE unfied encoders (Zhou et al., 2024). which suffer from poor understanding due to limited semantics. Subsequent methods decouple the encoder, using CLIP for understanding and VQ/VAE for generation (Wu al., 2024a), which improves per- formance but increases complexity. To simplify this, recent approaches build unified encoders upon CLIP. Some quantize CLIP features for reconstruction (Wu et al., 2024b; Qu et al., 2024; Ma al., 2025a), causing semantic degradation. Other methods freeze CLIP and train a diffusion decoder generate images conditioned on CLIP features (Sun et al., 2024b). However, since CLIP fea- tures lose image details, the reconstruction quality is poor. In contrast, UniLIP can maintain CLIP's understanding capability while achieving excellent reconstruction.
Architectures for Unified MultiModal Understanding and Generation. Early methods share weights between tasks (Team, 2024), leading to training instability. Later methods adopt Mixture- of-Experts (MoE) to decouple tasks, resolving conflicts but doubling parameters (Liao et al., 2025; Deng et al., 2025). To improve integration, the pioneering work DreamLLM (Dong et al., 2023) first proposes learnable queries to bridge MLLMs and diffusion models, facilitating synergistic joint training. MetaQuery (Pan et al., 2025) further extends this to connect frozen MLLMs with DiTs. BLIP3-o (Chen et al., 2025a) adopts similar framework but uses CLIP features as diffusion tar- gets for better prompt alignment. While UniLIP also uses learnable queries, we introduce a dual- condition design to address the inability of prior query-based methods in editing.
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Seatructtio-Guideed Image Editing. Early methods (Brooks et al., 2023; Zhang al., 2023) employ modular pipelines, using MLLMs to generate prompts or spatial cues that guide editing. To enhance semantic control, many approaches (Liu al., 2025; Wu et al., 2025a) utilize MLLMs to extract rich semantic embeddings from both image and text, thereby conditioning the generative backhone for more faithful and controllable edits. However, since CLIP in MILMS cannot capture pixel details, these methods still require a VAE for supplementary. Uni UniWorld-V1 (Lin et al., 2025a) finds that Lig can preserve consistency at high resolution, but still relies VAE features for generation. In contrast, UniLIP can achieve consistent editing by adapting CLIP for reconstruction, maintaining coherence without resolution constraints and VAE dependence.
METHOD
Output
Reconstruction Loss
Pixel Decoder
A
Input
Output
CLIP
Reconstruction Loss
Projection
Pretrain
Self Distilaatin Loss
Pixel Decoder
CLIP
CLIP s
Input
Stage
Stage 2
Figure 3: Reconstruction training of UniLIP. Stage I trains the pixel decoder to learn reconstruc- tion based on frozen CLIP features, only producing blurry reconstruction results. Stage 2 enables CLIP training while incorporating self-distillation supervision to maintain the original capability. The projection between CLIP and the pixel decoder is omitted for simplicity.
3.1 FINETUNING CLIP FOR IMAGE RECONSTRUCTION
Image reconstruction demands fine-grained details, whereas CLIP primarily encodes high-level se- manties. Therefore, the central challenge to inject details without eroding original semantics. In pilot experiments, we reconstruct images directly from frozen CLIP features. The output is no- ticeably blurry (Figure 3, Stage 1), yet revealing that CLIP still retains very weak pixel-level cues. Guided by this insight, design two-stage training scheme to locate and then amplify CLIP's latent reconstruction capacity. During the second stage, we introduce self-distillation loss to con- strain distributional drift, thereby boosting reconstruction without impairing the original capacity.
Architecture. As illustrated in Figure 3, we employ an autoencoder architecture by pairing CLIP with a pixel decoder Dpix. using a projection h& to align feature dimensions. The reconstruction process is:
= (po
Specifically, the input image E HH processed by CLIP to extract features Fetip R H x W. xd where p is the downsample ratio and d is the feature dimension of CLIP. Then ho aligns the features the input dimension of D pixx obtaining Prooj E R # * xd Finally, the pixel decoder pis reconstructs i from Frog. In practice, hg is implemented using several MLPs.
Stage 1: Aligning the Pixel Decoder with CLIP. In this stage, we fix the CLIP and train the pixel decoder Dpi along with the projection hg. This configuration allows the model to fully leverage the information embedded within CLIP features while preventing unnecessary alterations. To acceler- ate the convergence training, the pixel decoder is initialized with pre-trained weights, while the projection is randomly initialized. The training objective is defined as:
Laagel = LMSE + LLPIPS
where LMsE represents pixel-wise reconstruction loss, and CLPIPS represents the perceptual loss computed using the I.PIS metric (Zhang et al., 2018).
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Connector
Multi-Modal Hidden States
Query Embeddings
LLM
DIT
Text Tokenizer
UniLIP
Query Embeddings
Noise
Replace the bird with squirrel sitting on the branch.
Trainable
Text Token
Frozen
Image Token
Overview of the dual-condition architecture in editing tasks. We replace Intern ViT with our UniLIP and freeze LLM to keep the original understanding performance. When generating images, UniLIP concatenates the multimodal hidden states and query embeddings as the input to the connector, which then serves as the condition for the diffusion transformer. This architecture effectively avoids information loss while leveraging the reasoning capabilities LLMs through query embeddings. For generation tasks, simply remove the input of the reference image.
Figure 4:
Stage 2: Refining CLIP with Self-Distillation. Since CLIP features inherently lack fine- ane-grained pixel details, the reconstruction quality achieved in the first stage is unsatisfactory. Therefore, in the second stage, we allow CLIP to undergo training. To prevent the features from rapidly diverging from their original distribution, we employ self-distillation approach to constrain feature alter- ations. The training objective this stage is:
Lungez = LMSE + LLPIPS + Fori F
where À is the weight for the distillation loss, Forg denotes the original CLIP features, and Fn is is sufficient (see Table II).
the fine-tuned CLIP features. We empirically find that setting Additionally, to further constrain parameter updates in CLIP, we set its learning rate to 0.1 times the global learning rate. We find that this strategy also helps preserve original abilities (Table 6).
Through our reconstruction training, we obtain a stronger UniLIP that not only supports reconstruc- tion but also retains its original understanding performance, even achieving higher scores on several benchmarks (see Table 1). These results indicate that we have partially overcome CL.IP's prior short- comings in capturing image details. UniL.PP is also well-suited to generation and editing: (1) UniL.P can achieve 32x compression ratio relative to the original image while remaining directly decod- able into pixels by a lightweight decoder. (2) UniLIP exhibits strong alignment with text, leading to better prompt consistency in generation and clearer correspondence between textual instruc- tions and visual inputs during editing: (3) UniLIP can encode both pixel-level details and high-level semantics, thereby supplying all the information of the reference image for reliable editing.
3.2 DUAL CONDITIONS FOR IMAGE GENERATION AND EDITING
As UniLIP produces continuous features, we follow methods like DreamLLM (Dong et al., 2023) and MetaQuery (Pan et al., 2025) to build the generation pipeline. These methods employ query embeddings to bridge MLLM with a diffusion transformer, where the query tokens serve as the conditions for the generation process. Notably, in knowledge-augmented generation, query embed- dings encode the MILM's reasoning result to implicitly define the target image content, resulting in better prompt alignment. However, such methods are rarely applied to editing tasks and have not demonstrated satisfactory quality robust quantitative metrics in editing benchmarks.
We identify the main bottleneck as the fixed number of query embeddings. For example, DreamLLM uses 64 tokens and MetaQuery uses 256 tokens, which limit the expressive capacity of the queries. This problem not obvious in text-to-image generation, since most text prompts in generation benchmarks are short and LLMs excel at compressing text information. However, in editing, query embeddings need to retain the details of one or more reference images. Therefore, using a fixed number of tokens is inevitable for information loss which leads inconsistency in the editing.
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Model
Res.
ratio
rFID
PSNR
SSIM
VILA-U (Wu et al., 2024b)
256
16
1.80
Tokeaflow (Qu et al., 2024)
256
16
1.37
21.41
0.687
DualViTok (Huang et al., 2025)
256
16
1.37
22.53
0.741
UniLIP
256
32
0.79
22.99
0.747
Emu2 (Sun et al., 2024b)
448
14
3.27
13.49
0,423
UniLIP
448
32
0.31
24.62
0.788
Table 2: CLIP-based reconstruction performance.
To address this information bottleneck, we propose a dual-condition architecture connect the MLLM and the diffusion transformer. As shown in Figure 4, in addition to the query embeddings, the multimodal hidden states of the MLLM are also used as conditions, forming dual conditions for cross attention in DiT. This supplements the information that cannot be captured by the query embeddings alone, such as pixel details from the reference images.
Our architecture successfully decouples generation and editing into complementary subtasks: the MLLM extracts rich contextual information and performs reasoning to determine the outcome, while the DiT synthesizes the image conditioned the extracted and inferred cues. Critically, the proposed dual-condition framework guarantees lossless information propagation during this de- coupling, thereby allowing UniLIP fully exploit its advantages in both generation and editing.
3.3 TRAINING SCHEMES
To construct unified model based on UniLIP capable of simultaneous understanding, generation, and editing, we adopt three-stage training strategy:
Stage 1: Connector Training. The primary goal of this stage to train the connector to effectively bridge the MILM and the DIT. The connector learns to align the MILM's output features with the DiT's original conditional feature space. Parameters of both the MILM and the DiT are frozen in this stage, and training conducted exclusively on generation tasks.
Stage 2:
Unified Generation and Editing Training. We utilize large-scale datasets to train the model on general generation and editing. Both the connector and the DiT are trained in this stage. Consistent with prior methods (Chen al., 2025a), the MILLM remains frozen, eliminating the need for expensive training understanding tasks.
Stage 3: Supervised Fine-Tume Tane-T (SFT). We employ high-quality instruction tuning data to en- hance generation fidelity and prompt alignment for both generation and editing tasks. During this phase, the connector and DiT continue to be optimized, while the MLLM remains frozen.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Architectures. We introduce two model variants, UniLIP- and UniLIP-3B, which are constructed by integrating the InterVL33 (Zhu et al., 2025) with the SANA (Xie al., 2024a). Specifically, UniLIP-IB combines Intem with SANA-0.6B, whereas UniLIP-3B utilizes Intern VL3-2B and SANA-1.6B. We directly employ the Intern from Intern VL3 as the CLIP and adopt the pixel decoder from DC-AE (Chen et al., 2024b). Our connector consists of 6 layers, maintaining the same structure and dimensions as the LLM Intern VL3. For the learnable queries, we set N=256.
Training Data. We use the image generation data from BLIP3-o (Chen et al., 2025a). The pre- training data includes 27M samples recaptioned by Qwen2.5-VI.-7B (Bai et al., 2025), 5M samples from CC12M (Changpinyo et al., 2021) and 4M synthesized images from JourneyDB (Sun et al., 2023). The instruction tuning data consists of 60K high-quality image-text pairs, generated by GPT- 4o (OpenAI, 2025). For editing, pretraining. In instruction tuning, we use ShareGPT-4o- Image (Chen et al., 2025b), which contains 46K editing samples. Since freeze the LLM, understanding data is not required.
data from Lel--mage-ddt-....
(Wang et al., 2025) for
Image Reconstruction Training. We train on the BLIP3-o pretraining data using A100 GPUs with batch size of 64 and a learning rate of le-4. The first stage runs for 300k steps x24x 224

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Published as a conference paper at ICLR 2026
Model
LLM Params
MME-P
MMB
MMMU
MM-Vet
SEED
A12D
MMYP
Und. Only
LLaVA-OV al., 2024a)
1238
52.1
29.1
65.5
InterVVL2.5 (Chen al. 2024c)
IB
70.7
41.2
48.8
69.3
31.3
IntemVL33 (Zhu et al., 2025)
IB
1492
72.6
43.4
59.5
71.1
69.4
67.3
IntemVL2.55 (Chen et al., 2024c)
74.7
43.6
60.8
74.9
IntemV1.3 (Zhu
2025)
1.8B
1633
80.6
48.2
62.2
75.0
78.5
72.7
Qwen2.5-VL (Bai et al., 2025)
3B
79.1
53.1
61.8
81.6
Emu3-Chat (Wang et al., 2024c)
1244
58.5
31.6
37.2
68.2
70.0
36.6
Und. and Gen.
Chameleon (Team, 2024)
35.7
28.4
8.3
0.0
VILA-U (Wu et al., 2024b)
7B
1336
66.6
32.2
27.7
56.3
22.0
MetaMorph (Tong et al., 2024b)
75.2
41.8
48.3
SEED-X (Gl et al., 2024)
13B
1457
70.1
35.6
43.0
66.5
TokenFlow-B (Qu et al., 2024)
13B
1354
55.3
34.2
22.4
60.4
54.2
Show-D (Xic et al., 2024b)
1097
26.7
ILLUME (Wang al., 2024a)
1445
75.1
38.2
37.0
71.4
Janus-Pro Pro (Chen et al., 2025c)
7B
1567
79.2
41.0
50.0
72.1
Harmon (Wu et al., 2025c)
1,5B
1155
65.5
38.9
67.1
MetaQuery-B (Pan
2025)
1238
58.5
29.1
BAGEL (Deng al., 2025)
3B
1610
79.2
43.2
48.2
54.7
BLIP3-o (Chen et ul., 2025a)
4B
1528
78.6
46.6
60.1
73.8
TokLIP al., 2025b)
Tar (Han al., 2025)
7B
1571
74.4
39.0
73.0
UniLIP-1B
1B
1499
72.6
43.3
59.4
71.0
70.7
68.7
UniL.IP-3B
Table 3: Comparison with state-of-the-arts on visual understanding benchmarks.
GenEval Position
WISE Biology
Model
# Params
Counting
Overall
Cultural
Overall
Gen. Only
SDXL (Podell et al., 2023)
2.6B
0.39
0.15
0.55
0.43
0.44
0.43
FLUX.I-dev (Labs, 2023)
12B
0.75
0.68
0.82
0.48
0.42
0.50
PixArt-a (Chen et al., 2024a)
0.6B
0.44
0.08
0.48
0.45
0.49
0.47
Emu3-Gen (Wang al., 2024c)
0.34
0.17
0.54
0.34
0.41
0.39
SD3-Medium (Esser et al., 2024)
0.72
0.42
0.39
0.42
Sana-1.66 (Xie et al., 2024a)
Und. and Gen.
VILA-U (Wu et al., 2024b)
0.26
0.35
0.31
TokenFlow-XL (Qu al., 2024)
0.41
0.16
ILLUME+ (Huang al., 2025)
2.6B
Janus-Pro (Chen al., 2025c)
0.80
0.30
0.36
0.35
MetaQuery-B (Pan al., 2025)
1.6B
0.74
0.44
0.41
0.46
MetaQuery-XL (Pan al., 2025)
1.6B
0.80
0.56
0.49
0.55
Harmon (W al., 2025c)
I.5B+IB IB
0.66
0.74
0.76
0.38
0.37
0.41
BLIP3-o-4B (Chen al.. 2025a)
3B 1.4B
0.81
0.50
BLIP3-o-8B (Chen et al., 2025a)
7B 1.4B
0.84
0.62
BAGEL (Deng ct al., 2025)
7B
0.81
0.64
0.82
0.44
0.44
0.52
OpenUni-B (Wu et al., 2025b)
1B 0.6B
0.74
0.77
0.84
0.37
0.39
0.43
OpenUni-L. (Wu et al., 2025h)
2B 1.6B
0.77
0.75
0.85
0.51
0.48
0.52
Show-o2 (Xic et al., 2025)
0.33
0.39
0.39
(Han al., 2025)
UniLIP-1B UniLIP-3B
IB 0.6B
0.83
0.83
0.88
0.54
0.50
0.56
1.6B
0.66
0.60
0.63
Table 4: Evaluation of text-to-image generation ability on GenEval and WISE benchmark.
resolution. The second stage consists of 400k steps at 224x 224 followed by 100k steps at 448 448. We enable adversarial training (Karras et al., 2019) after 200k steps in the second stage. To further restrict parameter updates, the learning rate of CLIP is set to le-5 in the second stage.
Image Generation and Editing Training. Stages 1, 2, and 3 are trained for 50k, 200k, and 30k steps, respectively. All stages use a batch size of 512 with a cosine learning rate decay schedule ranging from le-4 to le-5.

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Published as a conference paper at ICLR 2026
Model
# Params
Add
Adjust
Replace
Remove
Bkg.
Style
Overall
GPT-4o (OpenAT, 2025)
4.61
4.33
4.35
3.66
4.57
4.93
4.20
MagicBrush (Zhang et al., 2023)
0.98
2.84
1.58
1.97
1.58
1.75
2.38
1,90
Instruct-P2P (Brooks et al., 2023)
0.9B
2.45
1.83
2.01
1.50
1.44
1.88
AnyEdit (Yu et al., 2025)
1.3B
3.18
2.95
2.47
2.23
2.24
2.85
2.45
UtraEdit (Zhao al., 2024)
2.0B
3.44
2,81
2.96
2.83
2.70
OmniGen (Xiao et al., 2025)
3.47
3,04
2.94
2.43
3.21
4.19
2.96
StepIX-EAd X-Edit (Liu et al., 2025)
7B+12B
3.14
3,40
2.41
3.16
4.63
3.06
ICEdit (Zhang et al., 2025)
12B
3.58
3.39
3.15
2.93
3.08
3.84
3.05
BAGEL (Deng al., 2025)
7B+7B
3.56
3.31
3.30
2.62
3.24
4.49
3.20
Uni UniWorld-V1 (Lin al., 2025a)
7B+12B
3.82
3.64
3.47
3.24
2.99
4.21
3.26
Janus-4o (Chen
2025b)
3.60
3.25
3.27
2.28
3.32
4.47
3.26
OmniGen2 (Wu al., 2025a)
3B+4B
3.57
3.06
3.74
3.20
3.57
4.81
3,44
UniL.IP-IB
IB+0.6B
4.11
3.58
4.30
3.97
4.00
4.87
3.81
UniL.IP-3B
2B+1.6B
4.29
3.90
4.44
4.10
4.14
4.80
3,94
Table 5: Evaluation of image editing ability on ImgEdit benchmark.
4.2 COMPARISON WITH STATE-OF-THE-ARTS
Image Reconstruction. Table compares the performance CLIP-based tokenizers on the Im- ageNet 50k validation set. Since our IB and 3B models employ vision encoders and pixel de- coders with identical model size, their reconstruction performance also nearly identical (see Ap- pendix B.1). For brevity, we only report the results for UniLIP in 1B model. At a x56x 256 resolu- tion, UniLIP outperforms previous quantized CLIP tokenizers, achieving a 0.58 rFID improvement over ViTT (Huang al., 2025). Notably, UniLIP has higher downsampling ratio, which en- hances the efficiency of subsequent generation tasks. At 448 x 448 resolution, we benchmark against Emu2 (Sun al., 2024b), which uses diffusion decoder to reconstruct 1024x 1024 images from 448 x48 inputs. For a fair comparison, we resize Emu2's outputs to 448 448. Thanks to our ded- icated reconstruction training. UniLIP significantly outperforms Emu2 with 2.96 improvement in rFID and an 11.13 gain in PSNR. These results suggest that methods relying on diffusion decoders to supplement details often struggle maintain fidelity the original image. In contrast, our approach more effectively preserves and reconstructs pixel-level details from the CLIP features.
Multimodal Understanding. We replace Int in Intern with our UniLIP for evaluation. The benchmarks include MME-P (MME- Perceptor))))))) (Fu et al., 2023), MMB (MMBench) (Liu al., 2024c), MMMU (Yue et al., 2024), MM- Vet (Yu al., 2023), SEED (SEED-Bende Img) (Li et al., 2024b). A12D (Kembhavi et al., 201 and MMVP (Tong et al., 2024c). As shown in Table 3, we compare UniLIP with both unified models and understanding-ont Only). Because our reconstruction training can effectively maintain the original capabilities, UniL.P can achieve otate-of-the-art understanding performance compared to previous unified models with similar scales. It is noteworthy that UniLIP's understanding performance significantly surpasses previous methods that quantize CLIP features, such as Tar (7B), VILA-U (7B), and TokenFlow (14B), even though our model size is much smaller.
models (indicated Und.
Image Generation. We primarily evaluate the model's image generation capabilities on two bench- marks: GenEval (Ghosh et al., 2023) and WISE (Niu al., 2025). GenEval examines how well the attributes of generated objects can be controlled by user instructions, including counting, posi- tion, and color attributes. WISE tests whether the model can understand text prompts that contain complex semantics and world knowledge. Table 4 presents the performance on GenEval and WISE benchmarks. Benefiting from our rich semantic features, the generation results of UniLIP align more effectively with text prompts, earning a score of 0.90 on the GenEval benchmark and 0.63 on the WISE benchmark. This result not only significantly outperforms models of similar size, such as Harmon, Janus-Pro, and MetaQuery, but is also comparable to larger models like BLIP3-o and BAGEL, indicating that UniLIP is a better choice for building unified model based on MI.MMs. Qualitative results presented in Figure 5 and Figure 10 (comparisons), which showcase UniLIP's ability to generate aesthetically pleasing images that closely adhere to user prompts.
Image Editing. We primarily evaluate image editing capabilities on ImgEdit-Bencd' (Ye et al., 2025). As shown Table 5, we achieve a highly competitive performance, with an overall score of 3.94, surpassing OmniGen2 (3.44) and UniWorld- V1 (3.26). This strong performance stems from the advantages of UniLIP's features, which are not only semantically rich and well-aligned with editing instructions but also retain fine-grained visual details. Our proposed dual-condition architec-
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Published as a conference paper at ICLR 2026
UniLIP
Image Generation
Create
Momen t
Image Editing
Pallaee the dog with ip largg pineapple
Remove the rainhow and cloud face paint from the image.
And flower garden Surrouncing the shed and change the trees blossom trees.
while wearing the same goggles.
95
123
Extract the metoreycle parked onggle
Adjust the image style an painting
Replace "123".
image.
brushetrokes.
Figure 5: Qualitative results of image generation and editing.
ture is designed fully leverage this rich information, enabling precise edits while preventing detail loss and maintaining consistency in unedited regions. The qualitative results in Figure showcase UniLIP's ability to accurately modify images while preserving the integrity of surrounding areas. Further visual comparisons are provided in the appendix in Figure 11.
4.3 ABLATION STUDY
Strategies in Reconstruction Training. We present an ablation of the core design choices in recon- struction training in Table 6. The first row shows the baseline where CLIP is directly finetuned with the reconstruction objective. Although this achieves the best reconstruction PSNR, the understand- ing performance drops significantly, with results several benchmarks falling zero. All three training strategies we adopt help preserve the understanding ability of the CLIP, with only a minor loss in reconstruction performance compared the baseline. Among them, self-distillation training proves to be the most effective; removing leads to 54.2 point decrease in MMBench.
Two stage Single stage. To verify the effectiveness of the two-stage training strategy. compare the training loss and performance of our approach against a single-stage baseline Fig- We observe that the single-stage method exhibits severe instability, characterized by initial distillation loss spike nearly double that of our method (0.0939 vs. 0.0497). Consequently, its con- vergence is significantly slower, requiring 3x more iterations to reduce the loss and 4x more to recover understanding performance. These results suggest that Stage 1 is critical for pre-aligning the pixel decoder; without this preliminary step, the initial misalignment between the unfrozen CLIP and the randomly initialized projection layer leads to gradient instability.
Dual-condition Architecture. Table presents an ablation of the dual-condition architecture in UniLIP-1BB IB. On the WISE benchmark, which requires knowledge-drive generation, query embed- dings make better use of the MLLM's reasoning ability and lead to 5-point improvement compared to using only the multimodal hidden states. In editing, however, relying solely query embeddings results in poor performance, achieving a score of only 3.38 on ImgEdit, which is lower than using just the multimodal hidden states. This is primarily because the query embeddings cannot effectively compress the information the reference image, resulting in inconsistency. The dual condition ar- chitecture combines the advantages of both approaches, thereby achieving optimal performance.

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Multimodal Published as a conference paper at ICLR 2026
Suppo Rocen loo segle Magee Aacinisn Single Stage:
12
4x Taster
0.58
Value
Our Stage2: IFO
Single Stape:
3x
0.52
0.50
Training Step
2002
506 1006 158h 2006 2006 3004 3336 200L 450c 500 5OOk 0004 600 7006
Training Step
(b) Recon. (rFID) and und. (MMVP) performance Figure 6: Comparison of training loss and performance curve between single-stage and two- stage method. Thanks to the pre-alignment in Stage I, our Stage 2 can converge much faster.
(a) Reconstruction and distillation loss curve.
Two Stage
Self- Distillation
Lr Decay
Reconstruction PSNR
Understanding MMVP
rFID
SSIM
MME-P
MMBench
A12D1
TextVQAt
0.43
26.01
0.819
124
47.3
27.9
0.29
25.28
0.804
709
18.4
50.0
50.0
7.5
x
0.28
25.11
0.801
1466
71.4
66.7
68.3
67.3
0.35
24.61
0.782
1478
72.0
67.3
74.0
0.31
24.62
0.788
1499
72.6
68.7
70.7
74.7
Table 6: Ablation of reconstruction training strategies on reconstruction and understanding performance. "Lr Decay" refers to setting the learning rate of CLIP to le-5.
Encoders for reference and target images. Table presents ablation on encoder choices for reference and target images UniLIP-1B. The target image encoder cannot be CLIP, because either produces blurry reconstructions or requires an additional DiT to generate images. Replacing the reference image encoder with CLIP (original Intern significantly decreases editing perfor- mance (from 3.81 to 3.42), as CLIP lacks pixel details, leading mismatches between generated and reference images (see Appendix Figure 12). Using VAE (DC-AE) as the target image encoder also reduces generation and editing quality, especially on WISE (from 0.56 to 0.48). These findings indicate that UniLIP achieves superior prompt alignment compared to VAE, which is consistent with the results in BLIP3-o (Chen et al., 2025a). Overall, UniLIP serves as more capable unified encoder than CLIP and VAE, offering richer pixel detail and enhanced instruction adherence.
5 CONCLUSION
In this paper, we propose UniLIP, a general framework that extends CLIP to support understand- ing, reconstruction, generation, and editing simultaneously, paving the way for more powerful and unified multimodal models. By introducing a carefully designed two-stage training scheme with a self-distillation constraint, UniLIP effectively overcomes the conventional trade-off between seman- tie comprehension and pixel-level detail preservation. Furthermore, the dual-condition architecture seamlessly bridges MLLMs and generative diffusion models, enabling image synthesis and editing with superior detail and consistency. We demonstrate the superiority UniLIP across a wide range of benchmarks, setting promising direction for unified multimodal foundation models.
Query
Reference
Target
GenEval
WISE
ImgEdit
WISE
ImgEdit
Hidden States
Embedding
CLIP
VAE
0.84
0.46
3.37
0.47
3.62
UniLIP
VAE
0.85
0.48
3.70
0.52
3.38
CLIP
UniLIP
0.88
0.53
3.42
0.56
3.81
UniLIP
UniLIP
0.88
0.56
3.81
Table 7: Ablation of dual condition architecture on WISE and ImgEdit benchmark.
Table 8: Ablation of image tokenizers for refer- ence (editing only) and target images in training.
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Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented in this paper are reproducible. The model configurations, datasets, hardware types, training and inference hyperparameters are de- scribed in detail in Section 4.1 and Appendix A. Moreover, the pre-trained models and datasets utilized all open-sourced and easily accessible.
ACKNOWLEDGMENTS
LW is supported by National Science and Technology Major Project (9922D0114902) and National Science Foundation of China (NSFC92470123, NSFC62276005).
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APPENDIX
A IMPLEMENTATION DETAILS
A.1 ARCHITECTURE
To enhance the alignment between image features and the LLM, the UniLIP architecture incor- porates both the visual encoder (CLIP) and the projection layers between the CLIP and the LLM (e.g.. the two-layer MLP in InternVL). Images are downsampled 28x (14x via CLIP, then 2x via pixel shuffle)). Since 28x upsampling decoder is unavailable, we use the closest option, 32x decoder (DC-AE-132:32), and interpolate the output to the original resolution. A three-layer MLP then bridges the dimensionality gap. projecting UniLIP's nigh-dimensiona features (e.g., 896 in IB) down to the 32 dimensions accepted by the pixel decoder.
A.2 TASK BALANCE
Since the amount of generative data is much larger than editing, we adjust the sampling ratio during joint training by setting the sampling proportion of editing data to ten times its data ratio. In this way, sampling ratio generative to editing data during training is approximately 2:1. During instruction tuning, we set the sampling proportion of editing data to three times its data ratio, making the sampling ratio of generation to editing data approximately 1:1.
A.3 CLASSIFIER FREE GUIDANCE
We apply classifier-fres frre guidance for both generation and editing. For generation, nullify the text prompt with a probability of 0.1 during training. For editing, we follow StepIX-Edt (Liu al., 2025) nullify the text prompt with probability of 0.1 during training but do not nullify the reference image. All tasks use a guidance scale of 4.5.
A.4 ADVERSARIAL TRAINING
In the second stage, enable adversarial training (Karras et al., 2019) after 200k steps, introducing a discriminator to improve the reconstruction fidelity. The training loss is formulated as follows:
Caage22 = LASE + LIPPP + AL + AGCGAN
where AG is the weight for the discriminator loss LGAN, which is implemented as a binary cross- entropy (BCE) loss. We follow (Kim et al., 2025) to set the Ag 0.1.
B ADDITIONAL MAIN RESULTS
B. IMAGE RECONSTRUCTION
Both the IB and 3B models employ InterVVIT300M as the vision encoder and DC-AE as the pixel decoder, differing only in their intermediate MLP layers. In the IB model, two-layer MLP projects features from 1024 to 896 dimensions, followed by a three-layer MI.P that maps them to 32 dimen- sions. The 3B model, in contrast, maps features from 1024 to 1536 dimensions before the final projection to 32. Therefore, the visual encoder and decoder parameters the IB and 3B models very close, at 472M and 484M, respectively. The UniLIP in 3B model also achieves strong reconstruction performance, with an rFID of 0.35, PSNR of 25. 25.02, and an SSIM of 0.790.
B.2 IMAGE GENERATION AND EDITING
We list full results of GenEval, WISE, and ImgEdit in Table 18, Table 19, Table 20.
B.3 INFERENCE SPEED
As shown in Table 9, UniLIP is significantly faster at image generation than unified models with similar parameter counts, with all latencies measured single A100 GPU using the same text
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Distillation Loss
0.04
Loss Value
0.03
FF/D
FRDD
MHVP
66.0
0.01
65.5
0.00
Training Step
Training
(a) Reconstruction and distillation loss curve.
(b) Recon. (rFID) und. (MMVP) performance.
Figure 7: Loss and performance curve in reconstruction training.
Model
Type
Resolution
Steps
Latency
Stage 2
GenEval
WISE
ImgEdit
3
Jamus-Pro- IB
AR
384
576
8.5
Harmon-1.50
MAR
448
64
46.1
x
v
0.87
0.53
3.77
BLIP3-o-4B
Diff.
448
30450
5.1
/
x
v
0.84
0,49
3.58
UniLIP-IB
Diff.
448
20
1.4
/
J
x
0.69
0.48
3.75
UniLIP-3B
Diff.
448
20
1.7
/
V
v
0.88
0.56
3.81
Table 9: Inference latency (s) comparison.
Table 10: Ablation of training stages.
prompt. Compared to autoregressive methods, UniLIP requires far fewer iterations (steps). Further- more, unlike BLIP3-o's complex, two-stage diffusion process. UniLIP generates images directly from its features via lightweight pixel decoder, yielding substantial speed advantage.
B.4 REASONING EDITING
To comprehensively assess UniLIP's editing capabilities, evaluated it on the RISE benchmark. Results in Table 12 show that our 3B model outperforms significantly larger models like Step Edit (7B+12B) and Bagel (7B+7B), confirming UniLIP's superiority in massoning-based editing.
B.5 LINEAR AND ATTENTIVE PROBE
We conduct both linear probe and attentive probe evaluations ImageNet. The results in Table 13 show that our UniLIP achieves classification performance comparable to the original Intern VIT. This validates the effectiveness of our reconstruction training and selffdistillation loss.
C MORE ABLATIONS
C.I RECONSTRUCTION TRAINING CURVE
Figure shows the stage two (224x224) training curves. Initially, the distillation loss rises sharply, suggesting interference with original understanding capabilities. After that, this loss consistently drops, while the understanding performance steadily improves to match the start level. Meanwhile, reconstruction loss remains stable and rFID steadily decreases (with a sharp drop at 200k steps due to adversarial training). We attribute this to the low information capacity required for expressing pixel details. For instance, standard dimension of VAE is only 16, significantly smaller than CLIP. This allows strong reconstruction by making only slight adjustments to the original CLIP features (as evidenced by the distillation loss of 0.02). As result, UniLIP effectively balances the dual objectives understanding and reconstruction.
C.2 GENERATION AND EDITING TRAINING STAGE
The training stage ablation Table 10 confirms that all stages are essential. The third instruction- tuning stage significantly improves GenEval scores due its dataset's similar prompt style. The
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Reconstruction
Understanding MMVP1
Type
Weight
rFID
PSNR
SSIM
MME-P1
MMBencc
A12D1
Tex
1.0
0.32
24.55
0.786
149
72.5
68.3
69.8
74.1
Cosine
0.35
24.82
0.798
1492
72.2
67.3
69.4
73.6
MSE
1.0
0.31
24.62
0.788
1499
72.6
68.7
70.7
74.7
Cls
1.0
0.36
25.09
0.805
820
53.6
54.7
58.4
53.0
MSR
0.1
0.28
25.03
0.801
1486
71.7
67.0
69.5
73.5
MSE
5.0
0.42
24.28
0.774
1511
72.8
68.3
70.6
74.3
Table 11: Ablation of distillation loss types and weights. "Cls" means classification loss.
Model
Size
Temporal
Causal
Spatial
Logic
Overall
Model
Linear
Attentive
OmniCen (Xiao et al., 2025)
3.8B
1.2
1.0
0.0
1.2
0.8
Prozen CLIP
79.8
82.6
X-Edit (Liu al., 2025)
128
2.2
3.5
UniL.IP
BAGEL (Deng et al., 2025)
7B + 7B
2.4
5.6
14.0
1.2
6.1
UniILIP-IB UniLIP-3B
IB+0.68
5.9
7.8
1.0
1.1
3.9
Table 13: Linear and at-
2B 1.6B
9.4
15.5
5.0
2.4
8.1
tentive probing results
Table 12: Evaluation of image editing ability on RISE benchmark.
ImageNet validation.
second stage primarily affects WISE and ImgEdit, highlighting the need for both extensive pretrain- ing and high-quality editing data. Finally, the initial alignment stage also important, likely because it prevents the randomly initialized connector from disrupting pretrained capabilities.
C.3 DISTILLATION LOss
Table 11 ablates the distillation loss type and weight. At fixed weight, MSE is most effective for preserving understanding, while classification loss is least effective because it provides only coarse, image-level supervision signals. Regarding weighting, low values are detrimental understanding, yet high values offer no significant gains, likely due to overfitting that degrades generalization and reconstruction. Therefore, MSE with weight of 1.0 achieves the optimal trade-off.
C.4 UNDERSTANDING THE QUERY EMBEDDINGS
We employ learnable queries primarily to enable mnowledge-augmented generation (e.g., for WISE benchmarks). In such tasks, prompts like "Draw Thanksgiving food" do not explicitly describe the visual details, so the model must rely on internal knowledge to deduce what to draw. To better illustrate the effect of query embeddings, we treat the output query embeddings as text embeddings and decode them into text. We observe that the decoded text can explicitly identify the target content. For instance, given the prompt "Thanksgiving food", the decoded text includes the word "turkey", which greatly simplifies generation for the DiT. In contrast, models lacking this mechanism generate incorrect foods. We provide detailed visual comparisons in Figure
C.5 QUERY LENGTH
We investigate the impact of the learnable query length, denoted as L,, within our dual-condition architecture. As presented in Table 14, the performance on the WISE benchmark exhibits a positive correlation with query length, reaching point of convergence around L 256. We hypothesize that longer queries effectively mimic an increased inference budget for textual reasoning. This expanded capacity enables the Large Language Model (LLM) to more comprehensively activate its internal knowledge, thereby facilitating more accurate image generation.
Query Length
Cultural
Time
Space
Biology
Physics
Chemistry.
Overall
0
0.43
0.50
0.62
0.44
0.58
0.35
0.47
0.48
0.52
0.62
0.54
0.40
0.50
0.56
0.58
0.54
256
0.54
0.58
0.70
0.50
0.62
0.46
0.56
512
0.56
0.57
0.67
0.48
0.60
0.44
0.56
Table 14: Ablation study on the impact of learnable query length on the WISE benchmark.
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Results w/ multimodal
Decoded text by query embeddings
Results w/ query embeddings
Prompt
hidden states
famous structure built in ancient Egypt as burial place.
...ancient burial tomb... Pyramid...tressie treasures the granite...
...Traditional food... Thanksgiving..turkke. vegetables.. pumpkin... turkey...
Traditional food for Thanksgiving in United States.
deiitinctive...eserr... camel...waer.... subvive...bbiltt
The animal known for its distinctive hump and ability to survive in deserts.
A national treasure animal of China.
trtassuur... China.... distinctive animal... panda..stone...natie .aatioaal..
Figure 8: Qualitative comparison between multimodal hidden states and query embeddings. We decode query embeddings back into text (masking unreadable tokens with ...). The results show high semantic consistency with both the prompt and the generated image, especially the red- highlighted terms.
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Method
Add
Adj.
Ext.
Repl.
Rmv.
Bkg.
Style
Hyb.
Act.
Overall
Edit Only
3.99
3.38
1.77
4.16
3.92
3.88
4.75
2.96
4.21
3.67
Joint Training
4.11
3.58
1.94
4.30
3.97
4.00
4.87
3.17
4.35
3.81
Ablation study on training strategies. We compare model trained solely on editing data against our joint training strategy. The joint approach leverages large-scale generation data to enhance editing performance across various sub-tasks.
Table 15:
C.6 JOINT TRAINING
We unify image generation and editing to exploit task synergy, specifically enabling generation capabilitiis to facilitate editing tasks. Given the scarcity of high-quality editing data (1.5M sam- ples) compared to generation data (36M), training solely on the former poses significant risk of overfitting. By incorporating large-scale generation data, the model captures more robust under- lying image distribution, leading to more realistic and consistent edits. To validate this, we train 1B-parameter variant using only the editing dataset. As demonstrated in Table 15, joint training significantly outperforms the "Edit Only" baseline across all metrics, confirming that generative knowledge is crucial for high-quality editing.
C.7 COMPARISON WITH DUAL-ENCODER METHODS
To demonstrate the superiority of our unified encoder over the dual-encoder architecture, we conduct ablation studies based on JanusFlow (Ma et al., 2025b). We construct a unified variant, UniLIP- JanusFlow, by replacing the original dual encoders (SigLIP and SDXL-VAE) with our UniLIP- SigLIP. Specifically, we first apply our two-stage reconstruction training to the SigLIP encoder within JanusFlow to enable reconstruction (UniLIP-Sigl..P) Subsequently, using JanusFlow's 's pre- trained weights, we fine-tune both the original dual-encoder baseline (JanusFlow) and our unified model
on the ShareGPT-4o-Image Chen al. (2025b) dataset for 10k steps.
We first evaluate the fundamental capabilities of the encoders as shown in Table 16. In terms of reconstruction, UniLIP-Sight SigLLPP achieves lower rFID (0.35) than the original SDXL-VAE (0.38) while maintaining comparable PSNR and SSIM, proving it can serve as a high-quality visual gener- ator. Crucially, this added capability does not compromise semantic understanding; UniLIP-SigLa preserves or slightly improves performance on multimodal benchmarks (e.g., MME-P, MMVP) compared the original SiIL.I. This confirms that approach effectively resolves the conflict between reconstruction and understanding tasks.
Reconstruction PSNR
Understanding MMVP
Model
rFID.
SSIM
MME-P
MMBench
A12D
TextVQAt
JanusFlow (Ma et al., 2025b)
0.38
26.79
0.827
1302.0
64.6
65.0
65.7
55.5
Jail.IP-JanusPll Flow
0.803
1305.4
64.8
55.8
Table 16: Reconstruction and understanding performance comparisons.
Next, we compare the performance on generation and editing tasks (Table 17). To extend JanusFlow for the editing task, we follow previous work (Chen et al., 2025b) by concatenating SigLIP and VAE features, resulting in a total of 1152 image tokens. In contrast, UniLIP-Janusllow utilizes single unified feature sequence of only 576 tokens. Despite the significant reduction in token count, UniLIP-JanusFie
consistently outperforms the dual-encoder baseline (+2.0 on GenEval, +5.0 on WISE, and +0.14 ImgEdit). These results demonstrate that the unified representation eliminates the feature misalignment inherent in dual-encoder systems and provides a more efficient and effective foundation for generative tasks.
D QUALITATIVE COMPARISONS
D. IMAGE RECONSTRUCTION
Figure illustrates UniLIP's superior reconstruction against other methods. Pairing a simple pixel decoder with frozen CLIP yields blurry outputs (third column). While Emu2 (Sun al., 2024b) gen-
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GenEval Two Obj.
WISE
ImgEdit
Model
Sgl. Obj.
Overall
Time
Space
Overall
Add
Repl.
Overall
JanusPlow (Ma el al., 2025b)
0.99
0.82
0.73
0.35
0.46
0.31
3.21
2.92
2.95
UniLIP-JanusElow
1.00
0.85
0.75
0.40
0.49
0,36
3.38
3.15
3.09
Table 17: Generation and Editing Performance. Models are fine-tuned on ShareGPT-4o-Imane We report representative sub-metrics (e.g.. Single/Two Object for GenEval, Time/Space for WISE, Add/Replace for ImgEdit) alongside the overall scores. UniLIP-JanusFlow consistently outperforms the dual-encoder baseline across both fine-grained and aggregate metrics.
erates clearer images by conditioning diffuusin model CLIP features, it sacrifices consistency due to CLIP's inherent information loss, evidenced by altered clock digits. TokenFlow (Qu et al., 2024) also exhibits blurring faces and text because of information loss from feature quantization. Conversely, UniLIP employs a carefully designed training strategy for CLIP and preserves continu- features, enabling to deliver reconstruction quality matching DC-AE (Chen et al., 2024b).
D.2 IMAGE GENERATION
Figure 10 shows the qualitative comparisons of image generation. UniLIP accurately renders po- sitional relationships and clear faces (first row), unlike Harmon (Wu et al., 2025c) (reversed posi- tions) other models (distortions/obscured faces). Our semantic reasoning excels in the fourth row, uniquely generating "the fastest land animal." Furthermore, UniLIP demonstrates superior aesthetic sensibility, with its final row output more faithfully capturing Monet's style than other methods.
D.3
IMAGE EDITING
Figure 11 highlights UniLIP's superior editing, excelling in instruction adherence and consistency. For instance, UniLIP uniquely extracts the motorcycle correctly the third row, where other models revert or erase the object. It also generates more natural content: in the final row, UniLIP seamlessly inpaints the erased car area based on background context, avoiding other models' prominent blurring artifacts and yielding much more natural result.
D.4 ABLATION OF REFERENCE IMAGE ENCODER
Figure 12 compares the editing results of CLIP and UniLIP. While CLIP successfully follows the editing instruction, it struggles preserve the subject's identity, texture, and background. In con- trast, UniLIP leverages reconstruction training to overcome this limitation, achieving greater fidelity.
E LIMITATION AND FUTURE WORK
Currently, the scale of UniLIP is limited to 3B, which is significantly smaller than models like XtepIX-Edit (19B) and Qwen-Image (28B). Besides, UniLIP can only support 448 x448 resolution. These limitations result in subpar performance on text rendering and editing, a common flaw in many existing approaches (Xie et al., 2025; Wu et al., 2025b: Han al., 2025). In the future, we will further scale up the model size and resolution to enhance text rendering-relater tasks.
F THE USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, we utilize Large Language Models (LLMs) solely for the purpose of language polishing, grammar correction, and improving readability. All core research aspects, including ideation, data analysis, and the formulation of conclusions, are conducted entirely by the authors. The authors take full responsibility for the final content of the manuscript.
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Original
UniLIP
Frozen CLIP
Emu2
TokenFlow
DC-AE
H
BUO
HUR
R896
8896
GBO
#890
8896
8896
CAMELOT
CAMELOY
OOWNNO
CAMFIDE
CAMFLO
CAMELOR
1/12/11
1/12/11
08
1/12/11
1/12/1
MON P
MON
24512 85
MDH 244
HON
MON 2
10:50 MUIIAAN
10:Co
1O-COLL 1123
10:50:3 11 MUGION
MUSMAAN
MUDMAN
MUDMAN
Figure 9: Qualitative comparison of image reconstruction.
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UniLIP
Harmon
Janus
OpenUni
BAGEL
BLIP3o
photo of a skateboard above person.
STOP
STOP
STOP
STOP
STIOP
photo of a pink stop sign.
image of circular emblem representing balance and harmony Taoist philosophy.
The land animal.
An abstract painting in the style of Jackson Pollock.
ENERGY DRINK
ESELIN
STRANOY BRUT BER
Energy drink with sliced fruit, berries and raindrops
A little fluffy tiger catched glascube roaring sitting in grass bbackrround wild nature and waterfall.
A sleeping cat with a summer straw hat in a English garden full of spring flowers, ambience like in the works of Beatrix potter, full of color, Claude Monet style.
Figure 10: Qualitative comparison text-to-image generation.
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Reference
UniLIP
UniWorld-VI V1
StepIX
BAGEL
OmniGen2
Replace dog the image large pineupple while keeping seated and wearing the same goggles.
Replace the red trams in the image with yellow school buses.
Extract the red motorcycle parked on the concrete surface in the image.
Add a small wooden cabin with chimney near the edge of the forest on the right side of the image.
Change concert stage background beach setting.
Change the indoor showroom environment the picture to a cozy children's hedroom with colorful wall murals and plush carpets.
Add a flower garden surrounding the shed and change trees in background to blossom trees.
Remove camper van the image and make the surrounding forest more seamless.
Figure 11: Qualitative comparison of image editing.
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Model
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attri.
Overall
Gen. Only
DALLE-2 (Ramesh ul.,
0.94
0.66
0.49
0.77
0.10
0.19
0.52
SDv2.1 (Rombach al. 2022)
0,98
0.51
0.44
0.85
0.07
0.17
0.50
DALLE-3 (Betker et al., 2023)
0.96
0.87
0.47
0.83
0.43
0.45
0.67
SDXL (Podell ul., 2023)
0.98
0.23
F..UX.I-dev (labs, 2023)
0.98
0.93
0.75
0.93
0.68
0.65
0182
PixArt-a (Chen et al., 2024a)
0.98
0.50
0.44
0.80
0.08
0.07
0.48
Emu3-Gen (Wagg et al., 2024c)
0,98
0.34
0.81
0.21
0.54
SD3-Medium (Esser
2024)
0.94
0.72
0.89
0.60
0.74
Sana-1.6B (Xic al., 2024a)
0.99
0.77
0.62
0.82
0.21
0.47
Und. and Gen.
LWM-7B (Liu al., 2024b)
0,93
0.41
0.46
0.79
0.09
0.15
0.47
SEED-X-13B (Ge et al., 2024)
0.97
0.80
0.14
0.49
TokenFlow-XL
al., 2024)
0.95
0.60
0,41
0.81
0.16
0.24
0.55
ILLUME (Wang ul., 2024a)
0.99
0.86
0.45
0.71
0.39
0.28
0.61
I.J.ME++ (Huang et al., 2025)
0.99
0.88
0.62
0.84
0.42
0.53
0.72
Emu3-Gen-8B (Wang al., 2024c)
0.99
0.80
0.45
0.66
Janus-Pro-7B (Chen al., 2025c)
0.99
0.89
0.59
0.90
0.79
0.66
0.80
MetaQuery-B (Pan et al. 2025)
0.74
MetaQuery-XL
2025)
Harmon-1.5B (Wu 2025c)
0.99
0.86
0.66
0.85
0.74
0.48
0.76
BLIP3-o-4B (Chen et al., 2025a)
0,81
BLIP3-o-8B (Chen et al., 2025a)
0.84
BAGEL (Deng et 2025)
0.99
0.94
0.88
0.64
0.63
OpenUni-B (Wu
2025b)
0.99
0.91
0.74
0.90
0.77
0.73
0.84
OpenUni-L. (Wu et al., 2025b)
0.99
0.91
0.77
0.90
0.75
0.76
0.85
Tar (Han et al., 2025)
0.98
0.92
0.83
0.85
0.80
0.65
0.84
UniLIP-1B
0.99
0.83
0.80
0.88
UniLIP-3B
0.95
0.84
0.91
0.86
0.84
0.90
Table 18: Evaluation of text-to-image generation ability on GenEval benchmark.
Model
Cultural
Time
Space
Biology
Physics
Chemistry.
Overall
Gen. Only
SDv1.5 (Rombach et al., 2022)
0.34
0.35
0.32
0.28
0.29
0.21
0.32
SDXL (Podell et al., 2023)
0.43
0.48
0.47
0.44
0.45
0.27
0.43
FLUX.1-dev (Labs, 2023)
0.48
0.58
0.62
0.42
0.51
0.35
0.50
PixArt-a (Chen ct al., 2024a)
0.45
0.50
0.48
0.49
0.56
0.34
0.47
SD3.5-large (Esser et al., 2024)
0.44
0.50
0.58
0.44
0.52
0.31
0.46
Playground-v2.5 (1. et al., 2024c)
0.49
0.58
0.55
0.43
0.48
0.33
0.49
Und. and Gen.
VILA-U-7B (Wu al., 2024b)
0.26
0.33
0.37
0.35
0.39
0.23
0.31
Banus-Pro-BB (Chen al., 2025c)
0.30
0.37
0.49
0.36
0.42
0.26
0.35
Emuu3-Gen88B (Wang al., 20224c
0.34
0.45
0.48
0.41
0.45
0.27
0.39
MetaQuery-BB (Pan al., 2025)
0.44
0.49
0.58
0.41
0.49
0.34
0.46
MetaQuery-XI. XL (Pan et al., 2025)
0.56
0.55
0.62
0.49
0.63
0.41
0.55
Harmon-1.55 (Wu al., 2025c)
0.38
0.48
0.52
0.37
0.44
0.29
0.41
BLIP3-o-4B (Chen al., 2025a)
0.50
BLIP3-o-4B (Chen al., 2025a)
0.62
BAGEL (Deng et al., 2025)
0.44
0.55
0.68
0.44
0.60
0.39
0.52
OpenUni-B (Wu al., 2025b)
0.37
0.45
0.58
0.39
0.50
0.30
0.43
OpenUni-L. (Wu al., 2025b)
0.51
0.49
0.64
0.48
0.63
0.35
0.52
UniL.IP-IB
0.54
0.58
0.70
0.50
0.62
0.46
0.56
UniL.IP-3B
0.66
0.60
0.70
0.60
0.68
0.43
0.63
Table 19: Comparison of world knowledge reasoning on WISE.
25

---

Published as a conference paper at ICLR 2026
Model
Add
Adj.
Ext.
Repl.
Rmv.
Bkg.
Style
Hyb.
Act.
Overall
GPT-4o (OpenAl, 2025)
4.61
4,33
2.9
4,35
3.66
4.57
4.93
3.96
4.89
4.20
MagicBrush (Zhang al., 2023)
2.84
1.58
1.51
1.97
1.58
1.75
2.38
1.62
1.22
1.90
Instruct-P2P (Brooks et al., 2023)
2.45
1.83
1.44
2.01
1.50
1.44
3.55
1.20
1.46
1.88
AnyEdit (Yu et al., 2025)
3.18
2.95
1.88
2.47
2.23
2.24
2.85
1.56
2.65
2.45
UltraEdit (Zhao et al., 2024)
3.44
2.81
2.13
2.96
1.45
2.83
3.76
1.91
2.98
2.70
OmniGen (Xiao et al., 2025)
3.47
3.04
1.71
2.94
2.43
3.21
4.19
2.24
3.38
2.96
StepIX-Edit (Liu ct al. 2025)
3.88
3.14
1.76
3.40
2.41
3.16
4.63
2.64
2.52
3.06
ICEdit (Zhang et al., 2025)
3.58
3.39
1.73
3.15
2.93
3.08
3.84
2.04
3.68
3.05
BAGEL (Deng et al., 2025)
3.56
3.31
1.70
3.30
2.62
3.24
4.49
2.38
4.17
3,20
UniWorld-V1 (Lin al., 2025a)
3.82
3.64
2.27
3.47
3.24
2,99
4.21
2.96
2.74
3.26
Janus-4o (Chen et al.. 2025b)
3.60
3.25
2.28
3.27
2.28
3.32
4.47
2.74
4.13
3.26
OmniGen2 al., 2025a)
3.57
3.06
1.77
3.74
3.20
4.81
2.52
4.68
3,44
UniLIP--B
4.11
3.58
4.30
3.97
4.87
3.17
4.35
3.81
UniLIP-3B
4.29
3,90
2.22
4.44
4.10
4.14
4.80
3.04
4.55
3.94
Table 20: Evaluation of image editing ability on ImgEdit benchmark.
Reference
CLIP
UniLIP
Reference
CLIP
UniLIP
Change the dragon blue.
Replace the animal the image with
Remove the person the foreground of the image. Turn the branches into witch's magic wand, Figure Qualitative comparison between CLIP and UniLIP as reference image encoder.
26


Reviews:
[{"review_id": "Hn7qzN75rE", "reviewer": "Reviewer_6pTW", "summary": "The paper presents UniLIP, a unified multimodal framework that adapts CLIP into a reconstructable and generative visual representation while preserving its strong semantic understanding. UniLIP aims to enhance reasoning and edit consistency by combining multimodal hidden states with learnable queries, and reports state-of-the-art results on image understanding, editing, and generation benchmarks.", "questions": "Refer to weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The two-stage training plus self-distillation scheme for CLIP is well-motivated and appears to strike a good balance between semantic preservation and detail fidelity.\n2. UniLIP demonstrates strong empirical performance across popular benchmarks in image understanding, editing, and generation.", "weaknesses": "1. UniLIP's training objective are heavily reconstruction-centric and largely pixel-level, while self-distillation predominantly constrains the representation not to deviate from the original CLIP distribution. This setup may limit the model’s ability to discover a better feature distribution for both understanding and generation. Why the self-distillation is sufficient for preserving its understanding-centered semantics, will any proxy task such as image classification better than distillation? \n\n2. The ablation of its dual conditioning is not convincing. Were results obtained by removing one of the two conditionings at inference from the same trained UniLIP model, or by training separate model variants that use only one conditioning pathway (multimodal hidden states or learnable queries)?\n\n3. The multimodal hidden states are modeled by the LLM in a casual way, thereby containing reasoning knowledge, why is UniLIP have to use learnable query to further enhance its reasoning knowledge? In what cases do multimodal hidden states alone fail to provide sufficient reasoning, and how do learnable queries remedy this?\n\nThe author could provide an ablation study on different learnable query length, to discover the effectiveness of the learnable query, and how the learnable queries complement the multimodal hidden states, and quantify their complementarity.\n\nThis paper could benefit from a more comprehensive image editing benchmarketing, such as KRIS and RISE", "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": "zmgEmjt5Iv", "reviewer": "Reviewer_eMyq", "summary": "This paper proposes a unified MLLM for understanding and generation. Through self-distilling, the inherent strong understanding ability of CLIP enoder is well-preserved with boost in reconstruction ability. Leveraging the proposed dual-condition framework that combines query embeddings and MLLM latents, the reasoning ability of MLLMs can be transfered to downstream generation and editing tasks, enhancing instruction following and complex semantics comprehension. Meanwhile, pixel details can be preserved thanks to the reserved MLLM latents.", "questions": "I am more concerned about comparing and illustrating the importance of the unified encoder with dual encoder baselines like Janus pro.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The proposed pipeline extends several previous works and is simple yet effective. Finetuning CLIP through self-distillation makes sense and proves effective, while direct optimization for reconstruction frustrates understanding. The dual-condition of mulitimodal hidden states and query embeddings makes downstream diffusion transformer performs better than using either. Comparisons among state-of-the-art models are conducted and analyses are convincing.\n1. Rigorous Ablation of the Two-Stage Training Strategy: The authors provide extensive ablation studies on the two-stage training approach and the self-distillation strategy. These experiments are crucial, demonstrating that directly fine-tuning CLIP for reconstruction causes catastrophic forgetting of comprehension capabilities. The ablation results (Table 6) confirm that the self-distillation loss is essential, preventing the understanding score (MMBench) from plummeting by 54.2 points. This work rigorously solves the key challenge of adapting CLIP for reconstruction without compromising its original semantic power.\n2. State-of-the-Art Performance Across Unified Benchmarks: UniLIP demonstrates superior performance across understanding, generation, and editing tasks, showcasing that it effectively integrates high-level reasoning and low-level detail.", "weaknesses": "1. Insufficient Literature Review on Query Embeddings: The dual-condition architecture relies on query embeddings to connect the MLLM and the diffusion transformer, following precedents like MetaQuery (Pan et al., 2025) and BLIP3-o (Chen et al., 2025a). However, the earliest proposal was made by DreamLLM (Dong et al., ICLR 2024 Spotlight).\n2. Lack of Explicit Architectural Comparison to Dual-Encoder Baselines: UniLIP successfully combines high-level semantics and low-level pixel details by adapting CLIP for reconstruction. This approach yields competitive results, surpassing Janus-Pro (7B) in certain understanding benchmarks (MMBench 80.7 vs 79.2). However, the manuscript does not explicitly compare the necessity of the unified CLIP adaptation (UniLIP) versus a conceptually simpler dual-encoder system (one encoder optimized for high-level tasks like LLaVA setting/understanding, and another specialized encoder for low-level detail/reconstruction). The paper's ablation only proves that the original CLIP is inadequate for reference image encoding in editing, but a dedicated comparison against a generalized dual-encoder setup as a structural baseline would strengthen the argument for UniLIP's architectural unification.", "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": "FZbJnGZ2IP", "reviewer": "Reviewer_6SkM", "summary": "This paper proposes UniLip, a novel two-stage training scheme for CLIP to capture both semantic information and pixel details, enabling unified multimodal understanding and generation. The scheme finetunes the CLIP model to optimize reconstruction quality while preserving the features in the original CLIP. Moreover, this paper proposes a dual-condition architecture for editing tasks, which supplements hidden states of MLLMs as conditions to provide pixel details.", "questions": "1. What is the benefit of unified CLIP for both generation and editing tasks, compared to training two separate models for these two tasks respectively to achieve similar performance?\n2. Comparison with BLIP3-o: What is the essential reason for BLIP3-o applying a complex pipeline (described in lines 147-151)?\n3. Why the stage 1, which solely trains the pixel decoder is necessary? Can we directly train both the CLIP and decoder module and skip stage 1? (The benefit of the two-stage is not significant in Table 6, maybe we can just increase the training time in one stage.)\n4. The generation and editing require a different training process; why not describe the training in the method section?\n5. How about other CLIP methods (e.g., BLIP3-o mentioned in the paper) perform on the reconstruction tasks?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper presents a training approach for CLIP that effectively balances semantic understanding and pixel-level detail, achieving SOTA performance in various multimodal tasks.\nThe presentation is clear and well-structured, with comprehensive experiments and ablation studies demonstrating the effectiveness of the proposed method.", "weaknesses": "The description of related works for unified multimodal models could be more detailed to better position the contributions of this work.\nThe method requires finetuning for the pixel decoder in both stages; the reason for this design choice is not well explained.", "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": "yygxyG1AH0", "reviewer": "Reviewer_E7dY", "summary": "This paper introduces UniLIP, a unified framework that extends CLIP to support multimodal tasks, including understanding, generation, and editing. UniLIP incorporates a two-phase training methodology featuring self-distillation, which enhances CLIP's capabilities for high-fidelity reconstruction without compromising its baseline comprehension performance. To improve reasoning and ensure consistency in generation and editing tasks, the authors have innovatively implemented a dual-condition architecture within the MetaQuery framework. Despite its relatively smaller size with 1B and 3B parameters, UniLIP demonstrates superior performance over larger models such as BAGEL (7B) and Uniworld-V1 (12B), achieving benchmark scores of 0.90 on GenEval, 0.63 on WISE, and 3.94 on ImgEdit.", "questions": "1. After undergoing two-stage reconstruction training, how does UniLIP perform on downstream tasks such as linear probe or attentive probe within the CLIP framework?\n2. Has the study explored any self-distillation losses other than MSE loss?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper effectively addresses the limitations in CLIP's reconstruction capabilities through a two-stage training process, transforming it into a unified visual encoder.\n2. This proposed dual-condition architecture utilizes the multimodal hidden states and query embeddings as the input, which can effectively avoid information loss while leveraging the reasoning capabilities of LLMs through query embeddings. \n3. This paper conducts extensive experiments on understanding, generation, and image editing. With only 1B and 3B parameters, UniLIP outperforms larger unified models such as BAGEL (7B) and Uniworld-V1 (12B), thereby demonstrating the efficacy of the proposed method.", "weaknesses": "1. Given that the dual-condition architecture is based on MetaQuery, the novelty appears limited.\n2. In Tab.6, the performance improvement in understanding appears to be primarily due to self-distillation. Additionally, \"Lr Decay\" seems to have a more significant impact compared to \"Two Stage.\"\n3. The ablation studies in Table 6 are not thorough. For instance, they fail to convincingly demonstrate the effectiveness of each stage in the two-stage reconstruction training.", "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)