VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

## Abstract

Abstract VILA-U is a U nified foundation model that integrates V ideo, I mage, La nguage understanding and generation. Traditional visual language models (VLMs) use separate modules for understanding and generating visual content, which can lead to misalignment and increased complexity. In contrast, VILA-U employs a single autoregressive next-token prediction framework for both tasks, eliminating the need for additional components like diffusion models. This approach not only simplifies the model but also achieves near state-of-the-art performance in visual language understanding and generation. The success of VILA-U is attributed to two main factors: the unified vision tower that aligns discrete visual tokens with textual inputs during pretraining, which enhances visual perception, and autoregressive image generation can achieve similar quality as diffusion models with high-quality dataset. This allows VILA-U to perform comparably to more complex models using a fully token-based autoregressive framework. Our code is open sourced at https://github.com/mit-han-lab/vila-u .

## 1 Introduction

In recent years, large language models (LLMs) have demonstrated superior capabilities in various language tasks. Their appealing properties like instruction following, zero-shot generalization, and few-shot in-context learning motivate researchers to combine them with vision models to build visual language models (VLMs) for multi-modal tasks. Many efforts ; ; have been put into this field, achieving remarkable performance on visual language understanding benchmarks. In these works, visual inputs are projected onto LLMs’ semantic space through a vision foundation model like CLIP to bridge two modalities by including text-image alignment training objectives.

In addition to visual understanding, another essential research direction in combining visual and language modalities is visual generation. There are two popular approaches for text-guided image generation. One approach employs diffusion models , a powerful tool for various generation tasks. The other line of work converts visual content into discrete tokens through vector quantization (VQ) and then leveraging autoregressive transformers for high-quality and diverse generation ; ; .

Witnessing the rapid advancements in both visual understanding and generation, an emerging trend is to unify these techniques into a single multi-modal framework. There are two main approaches to achieving such unification. Many VLMs ; ; ; maintain an understanding-oriented framework and offload the generation task to an external diffusion model. This disjoint approach adds complexity to infrastructure design. Available large-scale foundation model training pipelines and deployment systems have already been highly optimized for language modeling with next-token prediction. Designing a new stack to support diffusion models would incur significant engineering costs. To circumvent such costs, it is desirable to design a single end-to-end autoregressive framework for both image understanding and generation. There is a trend in VLMs ; that adopt VQ encoders to convert visual inputs into discrete tokens and treat them in the same next-token prediction manner as language data. However, replacing continuous tokens with VQ tokens in VLMs usually results in a severe performance drop in downstream visual perception tasks. Other works ; have to make various architectural modifications and conduct multi-modal training from scratch, which is computationally expensive.

In this work, we present VILA-U, an end-to-end autoregressive framework with a unified next-token prediction objective for both visual and text inputs that can achieve competitive performance on both visual language understanding and generation tasks, without the help of external components like diffusion models.
We identify two critical principles to unify vision and language modalities effectively and efficiently. (1) Existing end-to-end autoregressive VLMs cannot achieve competitive visual understanding performance because the discrete VQ tokens are trained solely on image reconstruction loss and are not aligned with textual inputs. Therefore, it is crucial to introduce text alignment during VQ vision tower pretraining to enhance perception capabilities. (2) Autoregressive image generation can attain similar quality as diffusion models if trained on a high-quality data corpus with sufficient size. Guided by these insights, VILA-U features a unified foundation vision tower that converts visual inputs into discrete tokens through vector quantization and aligns these tokens with textual inputs using contrastive learning. The multi-modal training of VILA-U takes advantage of a unified next-token prediction objective for both visual and textual tokens on a small-size high-quality image-text corpus.

We evaluate VILA-U on common visual language tasks, including image-language understanding, video-language understanding, image generation and video generation. VILA-U significantly narrows the gap in visual understanding performance between end-to-end autoregressive models and continuous-token VLMs, while introducing competitive native visual generation capabilities.

## 2 Related Work

Large Language Models (LLMs). LLMs based on pre-trained large-scale transformers has drastically revolutionized natural language processing field. Featuring gigantic model size and pre-training data corpus, LLM has achieved remarkable performance on various linguistic tasks. The development of open-source LLMs such as LLaMA , Mixtral and Vicuna has furthered nourished research on how to adopt LLM for complex language tasks. Besides excellent zero-shot generalizability to diverse domains, LLM is commonly finetuned on custom datasets for better performance on specific tasks. Instruction tuning ; ; also stands as a key step for better outputs in applying LLMs. In this work, we adopt the LLaMA-2-7B model as our basic LLM.

Visual Language Models (VLMs). Combining computer vision and natural language processing gives rise to VLM in this LLM era. In VLMs, researchers leverage vision foundation models such as CLIP , BLIP and CoCa to extract visual features, align with texts, and feed them into LLM to achieve the cross-modality understanding between texts and visual content. Building upon such progress, many VLMs ; ; ; have been designed and trained on extensive vision-language data to achieve remarkable performance on visual understanding and reasoning tasks. VLMs are divided into two types. (1) BLIP-style VLMs ; ; ; ; ; utilizes cross attention mechanism to fuse language and visual information and optionally apply perceivers  to downsample visual tokens. (2) LLaVA-style VLMs ; ; ; ; ; ; ; ; ; ; ; converts visual inputs to tokens (patches) and pass them through ViTs. The output of ViTs undergoes MLP layers and gets aligned to the language space.
In this work, we aim to develop a VLM with visual understanding capacities comparable to prior works, while also possessing the new capacity of visual generation.

Unified Visual Language Models. Numerous efforts have been made to develop unified visual language models capable of generating both text and visual content, including images and videos. There are two mainstream methods to generate visual content in VLMs. Many works ; ; ; ; combine VLMs with diffusion models like Stable Diffusion for high-quality image generation. Other works ; ; ; ; adopt VQGAN-based vision encoders to convert visual inputs into discrete tokens and make LLMs learn to predict them.
In this work, we design our framework based on the autoregressive next-token prediction method for visual generation and make our VLM learn to generate visual content effectively and efficiently.

## 3 Methods

Figure: Figure 1: An overview of our framework’s multi-modal training and inference process. Visual inputs are tokenized into discrete tokens and concatenated with textual tokens to form a multi-modal token sequence. All tokens are involved in our next-token prediction process, enabling a unified training objective. During inference, the output tokens are decoded by our text detokenizer or vision tower decoder to yield multi-modal content.
Refer to caption: x1.png

This work proposes a multi-modal framework that aims to unify visual and language modalities efficiently and effectively. The key components enabling such unification are a unified foundation vision tower that converts visual inputs into discrete tokens aligned with text, and a unified multi-modal generative training procedure. An overview of the main multi-modal training and inference process within our framework is depicted in Figure 1.

### 3.1 Unified Foundation Vision Tower

To support diverse visual understanding and generation tasks, we first build a unified foundation vision tower to provide appropriate visual features. We propose to include text-image contrastive loss and VQ-based image reconstruction loss in our vision tower training, empowering the text alignment and discrete tokenization abilities for our vision tower. As depicted in Figure 2, the features extracted from images are primarily discretized through residual quantization. Then in one route, the discrete visual features are fed into a decoder to reconstruct the image and compute the reconstruction loss; on the other route, we compute the image-text contrastive loss between the discrete visual features and the textual features provided by a text encoder. With this training procedure, the vision tower learns to extract discrete features suitable for both understanding and generation in our VLM.

#### Unified Training Recipe.

A straightforward combination of contrastive and reconstruction loss cannot converge. This is because alignment and reconstruction tasks require high-level semantic and low-level appearance features, respectively. Training the entire vision tower from scratch with both objectives could induce conflicting goals. In practice, we observe that training the vector-quantized vision tower from scratch with both image reconstruction and contrastive loss results in a mere 5% Top-1 accuracy for zero-shot image classification on ImageNet  after several epochs of training.

To address this issue, we experiment with different training recipes and find the following solution to be most effective. Instead of learning both objectives simultaneously, our training recipe suggests first equipping the model with text-image alignment ability and then learning reconstruction while maintaining alignment ability. We initialize the vision encoder and text encoder with pretrained weights from the CLIP model to ensure good text-image alignment. Next, we freeze the text encoder and keep all vision components trainable using both contrastive and reconstruction loss. The contrastive loss maintains alignment ability, while the reconstruction loss develops reconstruction ability. This training approach converges quickly and yields strong performance. The pre-trained CLIP weights contain learned high-level priors, which are difficult and computationally expensive to learn from scratch. Initializing with these weights enables the binding of low-level and high-level features much faster and more tractably for the vision encoder. With this training recipe, we can efficiently train a vision tower that exhibits both good text alignment and image reconstruction abilities. We use weighted sum to combine the text-image contrastive loss and VQ-based image reconstruction loss:

$$ $\displaystyle\mathcal{L}_{total}=w_{contra}\mathcal{L}_{contra}+w_{recon} \mathcal{L}_{recon}$ (1) $$

In our experiments, we pick $w_{contra}$ = 1 and $w_{recon}$ = 1.

Figure: Figure 2: Overview of our unified foundation vision tower. Given input images the features extracted by the vision encoder are discretized using residual quantization. Then the discrete vision features are meanwhile put into the vision decoder to reconstruct images and used to perform the text-image alignment. During this process, the reconstruction loss and contrastive loss are computed to update the vision tower, endowing it to produce discrete visual features with text alignment.
Refer to caption: x2.png

#### Discussion: Failed Training Recipes.

We experiment with numerous training recipes and find none to be as effective as our final approach. We list four alternative recipes and discuss their shortcomings compared to our final recipe: (1) Load pre-trained CLIP weights into the text encoder only; (2) Load pre-trained RQ-VAE weights for the vision encoder and decoder while training other parts from scratch; (3) Freeze the vision encoder; (4) Make the text encoder trainable.

Recipes 1) and 2) fail due to the lack of pre-trained CLIP weights for the vision encoder. Training a CLIP model from scratch typically requires numerous GPU days with a large global batch size (e.g., 32k). However, VQ-based reconstruction training necessitates a relatively small global batch size (e.g., 512) for steady improvement. With such a small batch size, training a text-aligned vision tower from scratch would be prohibitively time-consuming and resource-intensive.

Recipe 3) fails because freezing the vision encoder prevents it from learning the low-level features essential for reconstruction. In this case, the burden of reconstruction falls entirely on the vision decoder, but it is impossible to reconstruct images well using only semantic features.

Recipe 4) fails because the quantized features are chaotic during the initial training steps, and the contrastive loss disrupts the text encoder weights, slowing down the entire training process.

In contrast, our final training recipe leverages pre-trained CLIP weights for the vision encoder, enabling it to maintain learned semantic features rather than grasping them from scratch. This allows us to train with a small batch size while keeping the vision encoder trainable, facilitating the learning of low-level features for reconstruction during training.

#### Residual Vector Quantization.

Our visual features are discretely quantized, so their representation ability heavily depends on the code size used in our quantizer. Since we hope they contain both high-level and low-level features, we need more capacities in their vector feature space, making a larger code size necessary for good performance in downstream tasks. However, too many codes for each image will result in too many tokens for LLM to produce in the visual generation process, incurring much latency.
So in an attempt to increase the vector feature capacity and meanwhile maintain a reasonable number of tokens for LLM, we adopt a residual vector quantization method following RQ-VAE to discretize a vector $\mathbf{z}$ as $D$ discrete codes:

$$ $\displaystyle\mathcal{R}\mathcal{Q}(\mathbf{z};\mathcal{C},D)$ $\displaystyle=\left(k_{1},\cdots,k_{D}\right)\in[K]^{D},$ (2) $$

where $\mathcal{C}$ is the codebook, $K=|\mathcal{C}|$ and $k_{d}$ is the code of $\mathbf{z}$ at depth $d$. Starting with $\mathbf{r}_{0}=\mathbf{z}$, we recursively perform vector quantization by

$$ $\displaystyle k_{d}$ $\displaystyle=\mathcal{Q}\left(\mathbf{r}_{d-1},\mathcal{C}\right),$ (3) $\displaystyle\mathbf{r}_{d}$ $\displaystyle=\mathbf{r}_{d-1}-\mathbf{e}\left(k_{d}\right),$ $$

for each depth $d=1,2,\cdots,D$, where $\mathbf{e}$ is the codebook embedding table and $\mathcal{Q}$ is the standard vector quantization:

$$ $\displaystyle\mathcal{Q}(\mathbf{z};\mathcal{C})=\underset{k\in[K]}{\arg\min} \|\mathbf{z}-\mathbf{e}(k)\|_{2}^{2}.$ (4) $$

The quantized vector for $\mathbf{z}$ is the sum over the depth dim: $\widehat{\mathbf{z}}=\sum_{i=1}^{D}\mathbf{e}\left(k_{i}\right)$. Intuitively, in each depth we choose a code to reduce the quantization error. So compared to the standard vector quantization methods, we have $D$ codes to quantize one vector, allowing for finer approximation and larger feature space. During multi-modal training and inference, LLM only needs to predict the code embedding, with codes in different depth sequentially produced by a depth transformer taking the code embedding as the initial input, as we will introduce in Section 3.2. So with this residual quantization, we can enhance the representation capability of our vision tower while incurring little latency.

### 3.2 Unified Multi-modal Generative Pre-training

Figure 1 presents an overview of our unified multi-modal pre-training process. Our vision tower encoder processes visual inputs sequentially, generating a 1D token sequence. This sequence is then concatenated with text tokens to form a multi-modal sequence. To distinguish between modalities and enable visual content generation, we insert special tokens: <image_start> and <image_end> at the start and end of image tokens, and <video_start> and <video_end> at the start and end of video tokens. Video tokens are the direct concatenation of multi-frame image tokens.

Pre-training data form. In terms of unified pre-training data, we leverage different concatenation forms between text and visual tokens to facilitate both understanding and generation. We use [image, text], [text, image], and [text, video] forms, with supervision loss added only on the latter modality in each pair to avoid unconditional content generation and promote modality alignment. We also employ an interleaved text and image concatenation form for enhanced understanding, with supervision loss applied solely to the text. Notably, we exclude the [video, text] form during pre-training for efficiency reasons, as we find incorporating it during supervised fine-tuning effectively yields excellent video understanding ability.

Training Objective. Since both visual tokens and text tokens are discrete, we can train our LLM with the general language modeling next-token prediction objective. However, due to the use of residual quantization for visual tokens, the training objectives for text and visual tokens differ slightly. For text tokens, the negative log-likelihood loss is calculated as

$$ $\displaystyle\mathcal{L}_{\text{text}}=-\sum_{i=1}^{T}\log P_{\theta}\left(y_{ i}|y_{<i}\right),$ (5) $$

where $T$ is the length of the multi-modal sequence and $i$ only counts when the text token appears at position $i$.
For visual tokens, residual quantization introduces a depth-stacked structure of codes at each visual position $j$. To address this, we leverage the depth transformer introduced in RQ-VAE . Specifically, given the code embedding $h_{j}$ generated by the LLM for visual tokens at position $j$, the depth transformer autoregressively predicts D residual tokens ($k_{j1}$, …, $k_{jD}$). During training, the input of the depth transformer $v_{jd}$ at depth d is defined as the sum of the code embeddings of up to depth $d-1$ for $d>1$ such that

$$ $\displaystyle v_{jd}=\sum_{d^{\prime}=1}^{d-1}\mathbf{e}(k_{jd^{\prime}}),$ (6) $$

and $v_{j1}=h_{j}$. Thus, the depth transformer predicts the next code for a finer estimation of the feature $\boldsymbol{\hat{z}}_{j}$ based on the previous estimations up to $d-1$. Then the negative log-likelihood loss for visual tokens is

$$ $\displaystyle\mathcal{L}_{\text{visual}}=-\sum_{j=1}^{T}\sum_{d=1}^{D}\log P_{ \delta}\left(k_{jd}|k_{j,<d}\right),$ (7) $$

where $T$ is the length of the multi-modal sequence and $j$ only counts when a visual token appears at position $j$. During the multi-modal pre-training, the weights of the depth transformer are randomly initialized and updated together with the LLM.

## 4 Experiments

In this section, we introduce comprehensive experiments to evaluate the performance of our method on various visual understanding and generation tasks. Firstly, we outline our experimental setup, including the model architecture, training datasets, and evaluation benchmarks. Subsequently, we evaluate the performance of our unified foundation vision tower. Then, we compare our method with other popular VLMs on various visual understanding and generation benchmarks. Finally, we give some qualitative results.

### 4.1 Experimental Setup

In our experiments, we employ LLaMA-2-7B as our base language model. For the vision tower, we choose SigLIP-Large-patch16-256 / SigLIP-SO400M-patch14-384 as our vision encoder architecture, and adopt the residual quantizer, depth transformer as well as the decoder architecture from RQ-VAE . The quantizer codebook size is 16384. All images and videos are resized to a resolution of $256\times 256$ / $384\times 384$, with each image or video frame converted into a $16\times 16\times 4$ / $27\times 27\times 16$ code with the residual depth $D=4$ / $D=16$. We train our vision tower on COYO-700M and evaluate it for zero-shot classification and reconstruction performance on ImageNet . For visual understanding, we leverage 1M [image, text] data from ShareGPT4V , 6M interleaved text and image data from MMC4 . For visual generation, we incorporate 15M high-quality [text, image] data curated from our internal dataset and 1M [text, video] data from OpenVid datasets. Classifier-free guidance is employed for visual generation with a CFG value of 3.

For examining visual understanding ability, we evaluate our model on the widely adopted zero-shot image-based visual-language benchmarks including VQA-v2 , GQA , TextVQA , POPE , MME , SEED , MM-Vet and video-based visual-language benchmarks including ActivityNet , MSVD , MSRVTT , TGIF .

To evaluate the visual generation capability, we use MJHQ-30K and GenAI-Bench as our benchmarks. The former adopts the FID between generated images and 30K high-quality images to reflect the overall capability of image generation. The latter is a challenging image-to-text generation benchmark that reflects the comprehensive generative abilities of visual generation models. This benchmark is divided into two categories of prompts: basic skills, which include attribute, scene, and relation understanding in text inputs, and advanced skills, which encompass counting, differentiation, comparison, and logical relation understanding in text inputs.

### 4.2 Unified Foundation Vision Tower

We present the commonly used metrics reconstruction FID (rFID) and Top-1 accuracy for zero-shot image classification on ImageNet to measure the reconstruction and text alignment capabilities of the unified foundation vision tower in Table 1. Our model achieves significantly better reconstruction results than VQ-GAN. Our rFID is slightly inferior to that of RQ-VAE when using the same code shape. This is expected as the introduction of contrastive loss during training, aimed at enhancing image understanding, led to a decrease in reconstruction quality. For the text alignment capability, our unified vision tower achieves a Top-1 accuracy of 73.3 / 78.0 under 256 / 384 resolution. This demonstrates the exceptional text alignment capability of our unified vision tower. However, it is worth noting that both the rFID and Top-1 accuracy of the vision tower only serves as a medium indicator instead of directly linear correlated to the final performance of our whole multi-modal framework. The performance on visual understanding and generation tasks presented in the following sections is more important.

**Table 1: The reconstruction FID (rFID) and Top-1 accuracy for zero-shot image classification of our unified vision tower on ImageNet.**
| Model | Pretrained Weights | Resolution | Shape of Code | rFID$\downarrow$ | Top-1 Accuracy$\uparrow$ |
| --- | --- | --- | --- | --- | --- |
| VQ-GAN | – | 256 $\times$ 256 | 16 $\times$ 16 | 4.98 | – |
| RQ-VAE | – | 256 $\times$ 256 | 8 $\times$ 8 $\times$ 4 | 3.20 | – |
| RQ-VAE | – | 256 $\times$ 256 | 16 $\times$ 16 $\times$ 4 | 1.30 | – |
| Ours | SigLIP-Large | 256 $\times$ 256 | 16 $\times$ 16 $\times$ 4 | 1.80 | 73.3 |
| Ours | SigLIP-SO400M | 384 $\times$ 384 | 27 $\times$ 27 $\times$ 16 | 1.25 | 78.0 |

### 4.3 Quantitative Evaluation

**Table 2: Comparison with leading methods on image-based visual language benchmarks. Our performance is close to leading VLMs, surpassing many methods by a large margin under the same LLM size, even with a discrete visual token type. * indicates that images in the training split of these datasets are observed during VLM training.**
| Method | LLM | Visual Token | Res. | VQAv2 | GQA | TextVQA | POPE | MME | SEED | MM-Vet |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| LLaVA-1.5 | Vicuna-1.5-7B | Continuous | 336 | 78.5^∗ | 62.0^∗ | 58.2 | 85.9 | 1510.7 | 58.6 | 30.5 |
| VILA | LLaMA-2-7B | Continuous | 336 | 79.9^∗ | 62.3^∗ | 64.4 | 85.5 | 1533.0 | 61.1 | 34.9 |
| Unified-IO 2 | 6.8B from scratch | Continuous | 384 | 79.4^∗ | – | – | 87.7 | – | 61.8 | – |
| InstructBLIP | Vicuna-7B | Continuous | 224 | – | 49.2 | 50.1 | – | – | 53.4 | 26.2 |
| IDEFICS-9B | LLaMA-7B | Continuous | 224 | 50.9 | 38.4 | 25.9 | – | – | – | – |
| Emu | LLaMA-13B | Continuous | 224 | 52.0 | – | – | – | – | – | – |
| LaVIT | LLaMA-7B | Continuous | 224 | 66.0 | 46.8 | – | – | – | – | – |
| DreamLLM | Vicuna-7B | Continuous | 224 | 72.9^∗ | – | 41.8 | – | – | – | 36.6 |
| Video-LaVIT | LLaMA-2-7B | Continuous | 224 | 80.2^∗ | 63.6^∗ | – | – | 1581.5 | 64.4 | 35.0 |
| CM3Leon-7B | 7B from scratch | Discrete | 256 | 47.6 | – | – | – | – | – | – |
| LWM | LLaMA-2-7B | Discrete | 256 | 55.8 | 44.8 | 18.8 | 75.2 | – | – | 9.6 |
| Show-o | Phi-1.5-1.3B | Discrete | 256 | 59.3^∗ | 48.7^∗ | – | 73.8 | 948.4 | – | – |
| Ours | LLaMA-2-7B | Discrete | 256 | 75.3^∗ | 58.3^∗ | 48.3 | 83.9 | 1336.2 | 56.3 | 27.7 |
| Ours | LLaMA-2-7B | Discrete | 384 | 79.4^∗ | 60.8^∗ | 60.8 | 85.8 | 1401.8 | 59.0 | 33.5 |

**Table 3: Comparison with leading methods on video-based visual language benchmarks. The performance of our method is close to state-of-the-art VLMs, surpassing many methods under the same LLM size, even with a discrete visual token type.**
| Method | LLM | Visual Token | Res. | MSVD-QA | MSRVTT-QA | TGIF-QA | Activity Net-QA |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Unified-IO 2 | 6.8B from scratch | Continuous | 384 | 52.1 | 42.5 | – | – |
| Emu | LLaMA-13B | Continuous | 224 | – | 18.8 | 8.3 | – |
| VideoChat | Vicuna-7B | Continuous | 224 | 56.3 | 45 | 34.4 | – |
| Video-LLaMA | LLaMA-2-7B | Continuous | 224 | 51.6 | 29.6 | – | – |
| Video-ChatGPT | LLaMA-2-7B | Continuous | 224 | 64.9 | 49.3 | 51.4 | 35.2 |
| Video-LLava | Vicuna-7B | Continuous | 224 | 70.7 | 59.2 | 70.0 | 45.3 |
| Video-LaVIT | LLaMA-2-7B | Continuous | 224 | 73.5 | 59.5 | – | 50.2 |
| LWM | LLaMA-2-7B | Discrete | 256 | 55.9 | 44.1 | 40.9 | – |
| Ours | LLaMA-2-7B | Discrete | 256 | 73.4 | 58.9 | 51.3 | 51.6 |
| Ours | LLaMA-2-7B | Discrete | 384 | 75.3 | 60.0 | 51.9 | 52.7 |

Visual Understanding Tasks. Table 2 and Table 3 summarize the comparison between our method and other leading VLMs on the image-language and video-language benchmarks respectively. Compared to the mainstream choice of continuous visual tokens produced by foundation models like CLIP, the VQGAN-based discrete visual tokens have less alignment with text, thus harming VLMs’ performance on visual understanding tasks. With our unified foundation vision tower, our model can have a performance close to leading VLMs even with discrete visual tokens.

**Table 4: Comparison with other visual generation methods on MJHQ-30K evaluation benchmark.**
| Method | Type | #Images | FID$\downarrow$ |
| --- | --- | --- | --- |
| SD-XL | Diffusion | 2000M | 9.55 |
| PixArt | Diffusion | 25M | 6.14 |
| Playground v2.5 | Diffusion | – | 4.48 |
| Show-o | Discrete Diff. | 36M | 15.18 |
| LWM | Autoregressive | – | 17.77 |
| Ours (256) | Autoregressive | 15M | 12.81 |
| Ours (384) | Autoregressive | 15M | 7.69 |

**Table 5: Comparison with other visual generation methods on GenAI-Bench . The results show that our method outperforms previous autoregressive visual generation methods. For advanced prompts that require better text following ability to generate, our method can have a relatively small performance gap with diffusion-based methods, even with much less training data and time.**
| Pretrained Weights | Data size | Loss Type | Top-1 Accuracy | VQAv2 | POPE | MME | SEED | MM-Vet |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SigLIP-Large | 25M | Recon. | – | 57.7 | 75.1 | 937.7 | 38.7 | 15.3 |
| SigLIP-Large | 25M | Recon. + Contra. | 62.9 | 68.0 | 83.7 | 1219 | 50.4 | 20.8 |
| SigLIP-Large | 700M | Recon. + Contra. | 73.3 | 75.3 | 83.9 | 1336.2 | 56.3 | 27.7 |

### 4.4 Qualitative Evaluation

Figure: Figure 3: VILA-U can correctly caption videos and cover all the details, thanks to the text alignment of our vision encoder.
Refer to caption: x3.png

Visual Understanding. To validate the effectiveness of VILA-U in comprehensive visual understanding tasks, we apply it in several understanding and reasoning tasks, as some examples shown in Figure 3 and Figure 5. From the results, we can see the versatility of VILA-U in various tasks including visual captioning and visual question answering. Besides, our model has inherited some important capabilities from VILA including multi-image understanding, in-context learning, as shown in Figure 5 and Figure 6.

Figure: Figure 4: VILA-U has good visual question answering capability. The images and questions are from the test split of VQAv2 dataset.
Refer to caption: x4.png

Figure: Figure 6: VILA-U can correctly reason over multiple images.
Refer to caption: x6.png

Visual Generation. We present some examples of the visual generation results with VILA-U in Figure 7. We can see that our model can be employed in both image generation and video generation, even trained with a relatively small data corpus. In the given examples, our method can generate nice-looking images and continuous videos adhering to users’ input.

Figure: Figure 7: VILA-U can generate high-quality images and videos given text input.
Refer to caption: x7.png

## 5 Ablation Study

In this section, we delve into some key design components of our framework and design ablation experiments to show how they will influence the performance.

### 5.1 Impact of Contrastive Loss to Visual Understanding

We include contrastive loss in vision tower training, which endows it with the text alignment ability. During our multi-modal training, such text alignment ability is crucial in enhancing modality fusion and performance on downstream visual language tasks. We validate the importance of this alignment by training the vision tower with and without the contrastive loss, evaluating its impact on visual language understanding performance. For this ablation study, we randomly sample 25M data from COYO-700M to train the vision tower. For multi-modal training, we use ShareGPT4V and MMC4 without text-image and text-video data. The results of the first two lines in Table 6 demonstrate the crucial role of text alignment in achieving strong visual language understanding performance.
Scaling the dataset size from 25M to 700M further enhances performance, highlighting the importance of learning text alignment on a large-scale dataset.

**Table 6: Impact of contrastive loss to visual understanding.**
| Pretrained Weights | Data size | Loss Type | Top-1 Accuracy | VQAv2 | POPE | MME | SEED | MM-Vet |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SigLIP-Large | 25M | Recon. | – | 57.7 | 75.1 | 937.7 | 38.7 | 15.3 |
| SigLIP-Large | 25M | Recon. + Contra. | 62.9 | 68.0 | 83.7 | 1219 | 50.4 | 20.8 |
| SigLIP-Large | 700M | Recon. + Contra. | 73.3 | 75.3 | 83.9 | 1336.2 | 56.3 | 27.7 |

### 5.2 Impact of Contrastive Loss to Visual Generation

We conduct two experiments to demonstrate the influence of contrastive loss to generation performance. For efficiency, we conduct only text-to-image pretraining and utilize Sheared-LLaMA-1.3B instead of LLaMA-2-7B as the LLM. In the first experiment, we use the RQ-VAE as the vision tower, which has an rFID of 1.30. In the second experiment, we employ our unified vision tower. Results are shown in Table 5.2. Our Unified Vision Tower yielded slightly worse FID results than the RQ-VAE on MJHQ-30K, possibly due to its inferior rFID resulting from the introduction of contrastive loss.

Table: Table 7: Impact of contrastive loss to visual generation.

| Vision Tower | LLM | Resolution | rFID $\downarrow$ | FID $\downarrow$ |
| --- | --- | --- | --- | --- |
| RQ-VAE | Sheared-LLaMA-1.3B | 256 $\times$ 256 | 1.30 | 12.0 |
| Ours | Sheared-LLaMA-1.3B | 256 $\times$ 256 | 1.80 | 13.2 |

Table: Table 8: Impact of CFG.

| CFG Value | FID $\downarrow$ |
| --- | --- |
| 1.0 | 14.1 |
| 2.0 | 13.0 |
| 3.0 | 12.8 |
| 5.0 | 13.2 |

### 5.3 Impact of Classifier-free Guidance

We adopt classifier-free guidance during the visual content generation. We investigate the impact of the CFG value on our 256-resolution model. Results presented in Table 5.2 indicate that a CFG value of 3.0 yields the best FID score.

## 6 Conclusion

We present VILA-U, a novel and unified visual language model that integrates video, image and language understanding and generation tasks into one autoregressive next-token prediction framework. Our method is not only more concise than most VLMs that leverage additional components like diffusion models for unifying visual generation and understanding, but also demonstrates that autoregressive methods can achieve comparable performance to state-of-the-art VLMs. Our success is due to both a unified foundation vision tower that aligns discrete visual features with texts during pre-training and a high-quality dataset suitable for visual understanding and generation training. We believe VILA-U can serve as a general-purpose framework for diverse visual language tasks.

Limitations. There is still a performance gap in the visual understanding ability between VILA-U and state-of-the-art VLMs leveraging continuous visual feature. Besides, the visual generation quality is relatively low compared to state-of-the-art diffusion models. In future work, we will be committed to overcoming these limitations to build an advanced VLM that can achieve state-of-the-art performance in all kinds of visual language tasks.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - Please share missing details as mentioned in the weaknesses
- What are the number of image and video tokens going into the LLM? How many tokens are processed by the RQ-transformer and what is its size (the RQ-VAE paper has multiple different settings)?
- It would be interesting to see if the vision tower training results hold for a general VAE setup instead of an RQ-VAE since that would make the results even more broadly applicable
soundness: 4
strengths: - The paper's most interesting contribution is the unified vision tower exploration to unify generation and understanding and the appropriate ways to train such an encoder
- The approach is quite straightforward and the application of RQ-VAE allows for token efficiency while preserving more information
- VILA-U is close to SOTA on visual understanding tasks (image and video) with comparable models
- The model also fares well on image generation tasks and comes close to diffusion models
summary: - The paper presents VILA-U, a unified model for language, image and video understanding + generation
- The model is trained with an autoregressive next token prediction loss for all tasks
- The paper explores vision encoder choices to ensure understanding and generation performance
weaknesses: - The method chooses RQ-VAE for efficiency, but there isn't a discussion / results around this. How would the results look if the vision tower didn't use RQ-VAE? How important is the RQ-VAE?
- The generated images are relatively low-resolution (256 or 384px), especially since the RQ-VAE allows for increased efficiency in tokens
- The paper doesn't really discuss video implementation details. Video understanding and generation have a mismatch in FPS / durations they usually support, what does VILA-U support? There isn't a discussion around this.
- The paper claims to support video generation, but there are no quantitative results around this. The two qualitative examples are also very simplistic in Figure 7.

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confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Please refer to the weaknesses section.
soundness: 3
strengths: 1. VILA-U introduces a unified framework that handles both visual understanding and generation in a single autoregressive next-token prediction model. 

2. The model leverages a unified vision tower that uses contrastive learning to align discrete visual tokens with textual inputs, which enhances the model's visual perception and text-visual alignment capabilities.

3. The experiments indicate the state-of-the-art performance of VILA-U in both image generation and understanding.
summary: The paper presents VILA-U, a unified foundation model for visual understanding and generation that integrates image and language processing into a single autoregressive next-token prediction framework. Unlike traditional visual language models that rely on separate modules or diffusion models for generation, VILA-U employs a unified vision tower to discretize visual inputs, aligning them with textual tokens through contrastive learning. From the experiments, the authors show that VILA-U can achieve state-of-the-art performance in both image generation and comprehension.
weaknesses: 1. Missing the clarification between VILA-U and other tokenization-based multimodal models, like AnyGPT [1] and SEED-LLaMa [2]. Those models also used visual tokenizers to discrete the images and trained with causal language modeling loss. I noticed the authors cite the SEED-LLaMa in the line 102, but the claim of “In this work, we design our framework based on the autoregressive next-token prediction method for visual generation and make our VLM learn to generate visual content effectively.” does not the main difference between VILA-U and SEED-LLaMa.

2. One of the claimed contributions of this paper is about proposing the training strategy for the unified foundation vision tower. However, the training strategy seems similar to SEED [3], which also used contrastive loss between image embeddings and text embeddings. Can authors clarify the difference between the unified foundation vision tower and SEED?

3. Comparisons with other tokenization-based multimodal models [1,2] and Emu2 [4] are missing.

4. The limitation section, which is required, is missing.

[1] Zhan, Jun, et al. "Anygpt: Unified multimodal llm with discrete sequence modeling." arXiv preprint arXiv:2402.12226 (2024).

[2] Ge, Yuying, et al. "Making llama see and draw with seed tokenizer." arXiv preprint arXiv:2310.01218 (2023).

[3] Ge, Yuying, et al. "Planting a seed of vision in large language model." arXiv preprint arXiv:2307.08041 (2023).

[4] Sun, Quan, et al. "Generative multimodal models are in-context learners." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.

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confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: My biggest suggestion/question is related to the number 1 weakness described above. If the author could highlight the main contribution of the work that would make its positioning much easier. One positioning that was left out in the weakness section above is to position the work as the "first" in some regards. However, while autoregressive modeling of text + language is a burgeoning field, VILA-U is not the first model that performs autoregressive modeling of multiple modalities.
soundness: 2
strengths: 1. The unification of multiple modalities in the same architecture (with the same training objective) is a very important topic. The paper is a valuable contribution to this overall research program. In the current work, the choice of quantized image tokens for image representation makes the autoregressive modeling task more natural as the image modality is tokenized into discrete tokens much like language. This helps minimizes the amount of code development required for adapting existing LLM code bases to their multimodal counterparts.
2. The paper performed fairly complete evaluations (image-text, video-text, text-image, ) and ablation studies that include model backbone and training objective.
summary: The paper, VILA-U presents a unified framework of autoregressive multimodal generation and understanding. It achieves this by first training a vision encoder (discretized via RQ codebook) for text-conditioned image tokens (initialized from CLIP) and then training image+text data using autoregressive modeling. It presents a complete training recipe for creating autoregressive multimodal models, and the resulting model is benchmarked against a wide range of existing models across tasks (generation and understanding)
weaknesses: 1. It is not clear to me how to position the work in its novelty or effectiveness and this may be addressable with some rewriting. I see 3 potential angles
    1. Training effectiveness by leveraging pretrained networks. The authors motivates the work by emphasizing that existing methods that attempt to unify multimodal generation and understanding either require significant architectural modifications to their uni-modal counterparts, or training from scratch. However, this comparison seems not to play a central role in the subsequent discussions. If the effectiveness of the proposed method is reflected in ease of training, then readers would expect to see comparison of training time/compute for comparable performances. 
    2. Effective token representation of image modality as discrete tokens: VILA-U differs from prior work in its adoption of RQ-VAE embedding for images. However, if this is the main innovation, the choice of RQ, its superiority over alternative methods, the important of discontinuous embedding of images (as compared to, for example, continuous embedding as in LaViT) will need to be elevated.
    3. State-of-the-art performance: If the main contribution is instead just the shear effectiveness of the method. Then it should demonstrate this quantitative in the paper. Unfortunately, the comparison tables doesn’t seem to suggest that the VILA-U model is the state-of-the-art in most benchmarks. Perhaps it achieves Pareto frontier between understanding and generation tasks? Or outperforms other models for the same training compute/time? Either way I’m not clear what the main advantage of the current work is over others. 
2. The discussion around training recipe is very important and useful for practitioners. However, it lacks both quantitative and qualitative (with examples) comparisons of the different training recipes. With the conclusion seems to be use an aligned CLIP model for image encoder initialization, which doesn’t seem to be a novel finding. I would recommend either supporting the discussion with more evaluation (quantitive or qualitative, ideally both) or moving the discussion to the appendix.
3. The paper suffers from unsubstantiated claims ( neither references nor experimental support). I've highlighted a few statements that are very important for the message in the paper below:
    - "replacing continuous tokens with VQ tokens in VLMs usually results in a severe performance drop"
    - "A straightforward combination of contrastive and reconstruction loss cannot converge"
    - "both the rFID and Top-1 accuracy of the vision tower only serves as a medium indicator instead of directly linearly correlated to the final performance of our whole multi-modal framework."

---

confidence: 5
contribution: 3
initial_rating: 5
presentation: 3
questions: 1. The  solid experimental results of VILA-U have largely reignited my confidence in the autoregressive image-text unified modeling direction. However, why is there no comparison with other text-image unified modeling models such as \textbf{MM-Interleaved, SEED, and DEEM} on image understanding tasks? Ignoring the contributions of pioneers is not advisable.

2. The video generation experiments are insufficient. Why not compare with methods like \textbf{OpenSora} and \textbf{CogVideoX} on \textbf{VBench}?

3. The article is unclear in its expression; are the visual tokens features directly discretized by the visual encoder, or are they encoded by a large language model? I suspect it is the former.

4. VILA-U claims to have lower computational complexity and to avoid misalignment. While I recognize the importance of addressing misalignment, the claim of lower complexity requires experimental support. Specifically, compared to unified autoregressive image-text modeling models, using separate models like fine-tuning Stable Diffusion can also construct end-to-end autoregressive image-text modeling, which is more efficient in training and performs better. Moreover, utilizing existing mature acceleration schemes offers fast speeds. VILA-U should emphasize more on data cleansing quality and misalignment.

5. Lastly, and most critically, I hypothesize that the structural improvements of the model provide minimal benefits compared to previous autoregressive unified models, with the majority of improvements stemming from the engineered data cleansing. For instance, MMC4-Core contains 22.4M data while MMC4 has 375M, yet some research indicates that training with these two datasets yields similar outcomes. Large-scale datasets like MMC4 are of very low quality. However, using just 6M of data to achieve excellent results suggests that your data is meticulously filtered, yet the paper lacks any detail on the core contributions of data construction. Conducting experiments on the same data with other model structures like \textbf{DreamLLM} is necessary to demonstrate the efficiency of \textbf{VILA-U}. 

I will improve my rating score if my concerns are addressed.
soundness: 3
strengths: 1. The idea of VILA-U is very straightforward, and the experiments are solid. It significantly enhances the capabilities of end-to-end autoregressive multimodal models in visual-language tasks, bridging the gap between autoregressive multimodal models and the LLAVA series, while also excelling in image generation.

2. The structure of the VILA-U paper is simple and easy to read, and the model implementation is very easy to follow.
summary: Summary:

VILA-U is a foundation model that unifies video, image, and language understanding and generation. Unlike traditional models that use separate components for different tasks, VILA-U simplifies this by employing a single autoregressive framework. This reduces misalignment and maintains near state-of-the-art performance in both understanding and generating visual language content. Key factors for its success include a unified vision tower that aligns visual and textual inputs, enhancing perception, and the ability to achieve high-quality image generation similar to diffusion models.

Contributions:

1.  VILA-U strives for an end-to-end autoregressive model that handles both visual and textual inputs through a unified next-token prediction approach. This approach eliminates the need for external components like diffusion models, simplifying the infrastructure.
2.  VILA-U is tested across a range of tasks, including image-language and video-language understanding, as well as image and video generation. It demonstrates notable improvements, particularly in narrowing the gap between autoregressive and continuous-token models in visual understanding, while also offering robust visual generation capabilities.
weaknesses: 1.Regarding the issue of missing in context learning assessments, VILA-U has undergone extensive training on image-text sequences and can accept any interleaved layouts of images and text. Therefore, it should possess excellent contextual learning abilities. This work could be enhanced by conducting tests on its ICT capabilities.

2.The description of the data curation process is not sufficiently clear, making it uncertain whether the data was meticulously selected or randomly chosen. If it is the former, I suspect that most of the improvements stem from high-quality data engineering rather than advancements in model architecture.

Meta-review: VILA-U presents a unified foundation model that integrates video, image, and language understanding and generation within a single autoregressive next-token prediction framework. Unlike traditional visual language models that use separate modules for understanding and generating visual content, VILA-U employs a unified vision tower that aligns discrete visual tokens with textual inputs during pretraining to enhance visual perception. This innovative approach eliminates the need for additional components like diffusion models, simplifying the model architecture while maintaining high performance across tasks. 

The model's effectiveness is demonstrated through comprehensive experiments across multiple benchmarks, where it achieves performance comparable to state-of-the-art specialized models in both understanding and generation tasks. Key to its success is the unified vision tower's ability to capture both semantic and appearance features through residual quantization and a combination of reconstruction and contrastive losses during training. The model also demonstrates strong in-context learning capabilities and can handle interleaved sequences of images and text effectively.

Decision: Accept (Poster)