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Lance
Nishant2414/Lance
Lance is a any-to-any model from Nishant2414. Use it for the any-to-any task on the model card, and read the license before you ship it in a product. It is set up for Lance. The card lists the license as apache-2.0.
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At a glance
- Task
- Any-to-Any
- Library
- Lance
- License
- apache-2.0
- Access
- Public
- Created
- May 18, 2026
- Updated
- May 18, 2026
- SHA
- be3da40d
Base models
Model card
From the Hugging Face model README
<div align="center">
<img src="assets/logo/lance-logo.webp" alt="Lance logo" width="450">
<h1 align="center">
Lance: Unified Multimodal Modeling by Multi-Task Synergy
</h1>
<p style="line-height: 1.5; margin: 0;">
<span style="white-space: nowrap;">
<a href="https://scholar.google.com.hk/citations?user=FXxoQlsAAAAJ&hl=zh-CN&oi=ao" style="text-decoration: none; color: inherit; font-weight: 700 !important; display: inline;">Fengyi Fu</a><sup>*</sup>,
<a href="https://corleone-huang.github.io/" style="text-decoration: none; color: inherit; font-weight: 700 !important; display: inline;">Mengqi Huang</a><sup>*,✉</sup>,
<a href="https://scholar.google.com.hk/citations?user=9ER6nVkAAAAJ&hl=zh-CN&oi=ao" style="text-decoration: none; color: inherit; font-weight: 700 !important; display: inline;">Shaojin Wu</a><sup>*</sup>,
<span style="font-weight: 700 !important;">Yunsheng Jiang</span><sup>*</sup>,
<span style="font-weight: 700 !important;">Yufei Huo</span>,
<a href="https://guojianzhu.com/" style="text-decoration: none; color: inherit; font-weight: 700 !important; display: inline;">Jianzhu Guo</a><sup>✉,§</sup>
</span>
<br>
<span style="display: inline-block; margin-top: 0.4em;">
Hao Li,
Yinghang Song,
Fei Ding,
Qian He,
Zheren Fu,
Zhendong Mao,
Yongdong Zhang
</span>
<br>
<em>ByteDance</em>
<br>
<sup>*</sup> Equal contribution
<sup>✉</sup> Corresponding authors
<sup>§</sup> Project lead
</p>
<p>
<a href="https://lance-project.github.io/" style="text-decoration: none; margin: 0 8px;"><img src="https://img.shields.io/badge/Homepage-Lance-blue?style=flat" alt="Homepage"></a>
<a href="https://lance-project.github.io/assets/lance.pdf" style="text-decoration: none; margin: 0 8px;"><img src="https://img.shields.io/badge/Paper-arXiv-red?style=flat&logo=arxiv" alt="arXiv"></a>
<a href="https://github.com/bytedance/Lance" style="text-decoration: none; margin: 0 8px;"><img src="https://img.shields.io/badge/Lance-Codebase-536af5?color=536af5&logo=github" alt="Github"></a>
<br>
English | <a href="./Lance/blob/main/README_zh.md"><ins>简体中文</ins></a>
</p>
</div>
🌟 Highlights
Lance is a lightweight native unified multimodal model that supports image and video understanding, generation, and editing within a single framework.
- Efficient at 3B scale. With only 3B active parameters, Lance delivers strong performance across image generation, image editing, and video generation benchmarks.
- Trained from scratch. Lance is built with a staged multi-task recipe and trained entirely from scratch within a 128-A100-GPU budget.
🎨 Demo
Text-to-Video
<table> <tr> <td><a href="assets/text-to-video/videos/text-to-video-demo-01.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-01.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-02.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-02.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-03.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-03.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-04.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-04.gif" width="100%"></a></td> </tr> <tr> <td><a href="assets/text-to-video/videos/text-to-video-demo-05.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-05.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-06.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-06.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-07.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-07.gif" width="100%"></a></td> <td><a href="assets/text-to-video/videos/text-to-video-demo-08.mp4"><img src="assets/text-to-video/previews/text-to-video-demo-08.gif" width="100%"></a></td> </tr> </table>Video Editing
<table> <tr> <td><a href="assets/video-editing/videos/video-editing-demo-01.mp4"><img src="assets/video-editing/previews/video-editing-demo-01.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-02.mp4"><img src="assets/video-editing/previews/video-editing-demo-02.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-03.mp4"><img src="assets/video-editing/previews/video-editing-demo-03.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-04.mp4"><img src="assets/video-editing/previews/video-editing-demo-04.gif" width="100%"></a></td> </tr> <tr> <td><a href="assets/video-editing/videos/video-editing-demo-05.mp4"><img src="assets/video-editing/previews/video-editing-demo-05.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-06.mp4"><img src="assets/video-editing/previews/video-editing-demo-06.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-07.mp4"><img src="assets/video-editing/previews/video-editing-demo-07.gif" width="100%"></a></td> <td><a href="assets/video-editing/videos/video-editing-demo-08.mp4"><img src="assets/video-editing/previews/video-editing-demo-08.gif" width="100%"></a></td> </tr> </table>Multi-turn Consistency Editing
<div align="center"> <a href="assets/multi-turn-editing/videos/multi-turn-editing-demo-01.mp4"> <img src="assets/multi-turn-editing/previews/multi-turn-editing-demo-01.gif" width="100%"> </a> </div>Intelligent Video Generation
<table> <tr> <td><a href="assets/intelligent-video/videos/intelligent-video-demo-01.mp4"><img src="assets/intelligent-video/previews/intelligent-video-demo-01.gif" width="100%"></a></td> <td><a href="assets/intelligent-video/videos/intelligent-video-demo-02.mp4"><img src="assets/intelligent-video/previews/intelligent-video-demo-02.gif" width="100%"></a></td> <td><a href="assets/intelligent-video/videos/intelligent-video-demo-03.mp4"><img src="assets/intelligent-video/previews/intelligent-video-demo-03.gif" width="100%"></a></td> <td><a href="assets/intelligent-video/videos/intelligent-video-demo-04.mp4"><img src="assets/intelligent-video/previews/intelligent-video-demo-04.gif" width="100%"></a></td> </tr> </table>Video Understanding
<div align="center"> <table> <tr> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-vqa-01.mp4"> <img src="assets/video-understanding/previews/video-understanding-vqa-01.gif" width="100%"> </a> <p><strong>Question:</strong> How many times did the person launch objects on the table? Options: (A) 3 (B) 2 (C) 4</p> <p><strong>Response:</strong> (A) 3</p> </td> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-vqa-02.mp4"> <img src="assets/video-understanding/previews/video-understanding-vqa-02.gif" width="100%"> </a> <p><strong>Question:</strong> The person makes sets of repeated actions. How many distinct repeated actions did the person do? Options: (A) 2 (B) 3 (C) 4</p> <p><strong>Response:</strong> (A) 2</p> </td> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-vqa-03.mp4"> <img src="assets/video-understanding/previews/video-understanding-vqa-03.gif" width="100%"> </a> <p><strong>Question:</strong> In which direction does the purple sphere move in the video? Options: (A) Down and to the right. (B) Up and to the left. (C) Up and to the right. (D) The object is stationary.</p> <p><strong>Response:</strong> (A) Down and to the right.</p> </td> </tr> <tr> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-vqa-04.mp4"> <img src="assets/video-understanding/previews/video-understanding-vqa-04.gif" width="100%"> </a> <p><strong>Question:</strong> What is the unrealistic phenomenon displayed in the video? Options: (A) The man can manipulate time via phone. (B) Man grabs an object through a phone screen. (C) Chocolate transforms into different objects. (D) Visible means of propulsion enables flight.</p> <p><strong>Response:</strong> (B) Man grabs an object through a phone screen.</p> </td> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-caption-short-01.mp4"> <img src="assets/video-understanding/previews/video-understanding-caption-short-01.gif" width="100%"> </a> <p><strong>Question:</strong> Offer a succinct account of the culinary process shown in this video.</p> <p><strong>Response:</strong> Add tomato puree and mix it well with chicken pieces.</p> </td> <td align="left" valign="top" width="33%"> <a href="assets/video-understanding/videos/video-understanding-caption-long-01.mp4"> <img src="assets/video-understanding/previews/video-understanding-caption-long-01.gif" width="100%"> </a> <p><strong>Question:</strong> Provide a detailed description of the given video, capturing its key moments.</p> <p><strong>Response:</strong> In a sunlit meadow, a small tortoiseshell butterfly rests on a purple flower. A bee, with black and yellow stripes, lands on the same flower. The butterfly flaps its wings gently, while the bee busies itself, collecting nectar. The flower sways slightly in the breeze. The butterfly then takes off, and the bee follows, both heading to the next flower. The scene is a vivid display of insect interaction in a natural setting, with the colors of the butterfly and the bee contrasting against the green background of the meadow. The video captures this peaceful moment in a short 6-second duration.</p> </td> </tr> </table> </div>Text-to-Image Generation
<div align="center"> <img src="assets/text-to-image/text-to-image-overview.webp" alt="Lance text-to-image generation examples" width="100%"> </div>Image Editing
<div align="center"> <img src="assets/image-editing/image-editing-overview.webp" alt="Lance image editing examples" width="100%"> </div>Image Understanding
<div align="center"> <table> <tr> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-01.png" alt="Pie chart reasoning case" width="100%"> <p><strong>Question:</strong> Is the largest segment greater than sum of all the other segments?</p> <p><strong>Response:</strong> Yes, the largest segment in the pie chart is the blue one, which is the most populous segment.</p> </td> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-02.png" alt="Survey chart about border security priorities" width="100%"> <p><strong>Question:</strong> What percentage of respondents want better border security?</p> <p><strong>Response:</strong> 29%</p> </td> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-03.png" alt="License plate recognition case" width="100%"> <p><strong>Question:</strong> What is the license plate number of the car?</p> <p><strong>Response:</strong> The license plate number of the car is BX62 BFY.</p> </td> </tr> <tr> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-04.png" alt="Market research chart reasoning case" width="100%"> <p><strong>Question:</strong> According to the data from the proprietary market research, how much amount was spent on the promotional meetings and events during 1998?</p> <p><strong>Response:</strong> According to the data from the proprietary market research, the total amount spent on the promotional meetings and events during 1998 was approximately $1.3 billion.</p> </td> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-05.png" alt="Colosseum description case" width="100%"> <p><strong>Question:</strong> What is the appearance of the Colosseum in Rome, Italy?</p> <p><strong>Response:</strong> The Colosseum in Rome, Italy is a magnificent ancient amphitheater made of stone and concrete. It is characterized by its large, arched openings and its semi-circular shape. The Colosseum has been partially restored, and its original color has faded, but the architectural details, like the columns and the semi-circular arches, are still visible. The lighting in the image highlights the grandeur and historical significance of the Colosseum, making it stand out against the backdrop of the evening sky.</p> </td> <td align="left" valign="top" width="33%"> <img src="assets/image-understanding/cases/image-understanding-case-06.png" alt="Total solar eclipse case" width="100%"> <p><strong>Question:</strong> How does a total solar eclipse look like from Earth?</p> <p><strong>Response:</strong> A total solar eclipse looks like a dark circle with a bright, white outer edge. This is a result of the Earth's rotation and the Earth's shadow, which partially obscures the sun's light. The bright white edge of the eclipse is caused by the sun's high energy and its interaction with the Earth's atmosphere, while the dark part of the eclipse is due to the Earth's shadow and the surrounding air currents. The solar eclipse's shape, with its bright white edge and dark center, is similar to the shape of a full moon or a dark disk. It is a natural phenomenon that occurs in the atmosphere of the Earth and is an important part of the solar system.</p> </td> </tr> </table> </div>🚀 Installation
Recommended Environment
- Software: Python 3.10+, CUDA 12.4+ (required)
- Hardware: A GPU with at least 40GB VRAM is required for inference
Installation Steps
bash ./setup_env.sh
Download Model Weights
Please download all the necessary model checkpoints of Lance-3B (Huggingface Link) and place them in the downloads/ directory.
📚 Usage
Inference
Lance provides a unified command-line interface for all generation / editing / understanding tasks:
bash inference_lance.sh
- Before running, please configure the inference parameters at the top of
inference_lance.sh. - Supported tasks:
t2i,t2v,image_edit,video_edit,x2t_image, andx2t_video. You can modifyTASK_DEFAULT_CONFIGSininference_lance.pyto customize the default data samples for each task.
Available Tasks
| Task Name | Description | Example JSON |
|---|---|---|
t2v | Text-to-Video generation | config/examples/t2v_example.json |
t2i | Text-to-Image generation | config/examples/t2i_example.json |
image_edit | Image editing | config/examples/image_edit_example.json |
video_edit | Video editing | config/examples/video_edit_example.json |
x2t_image | Image understanding | config/examples/x2t_image_example.json |
x2t_video | Video understanding | config/examples/x2t_video_example.json |
For understanding examples:
config/examples/x2t_image_example.json: image understanding examples for visual question answering and image-based reasoning.config/examples/x2t_video_example.json: video understanding examples for video question answering and video captioning.
Parameters
You can configure the following hyperparameters at the top of the inference_lance.sh script:
| Parameter | Default Value | Description |
|---|---|---|
MODEL_PATH | "downloads/lance_3b" | Path to the downloaded Lance model weights. |
NUM_GPUS | 1 | Number of GPUs to use for inference. |
VALIDATION_NUM_TIMESTEPS | 30 | Number of denoising steps (e.g., 30 or 50). |
VALIDATION_TIMESTEP_SHIFT | 3.5 | Timestep shift parameter for flow matching scheduling. |
CFG_TEXT_SCALE | 4.0 | Classifier-Free Guidance (CFG) scale for text conditioning. |
VALIDATION_DATA_SEED | 42 | Random seed for generation reproducibility. |
NUM_FRAMES | 50 | Number of frames for video generation (Max: 121). Unused for image tasks. |
VIDEO_HEIGHT / VIDEO_WIDTH | 768 | Spatial resolution. Unused for editing tasks (determined by input image/video). |
RESOLUTION | "video_480p" | Base resolution preset (image_768res or video_480p). |
Gradio
python lance_gradio_t2v_v2t.py --gpus 0 --server-port 7860
Benchmarks
DPG-Bench Evaluation
<div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center"># Params.</th> <th align="center">Global</th> <th align="center">Entity</th> <th align="center">Attribute</th> <th align="center">Relation</th> <th align="center">Other</th> <th align="center">Overall</th> </tr> </thead> <tbody> <tr> <td align="center" colspan="8"><i>Generation-only Models</i></td> </tr> <tr> <td align="left">SDXL</td><td align="center">3.5B</td><td align="center">83.27</td><td align="center">82.43</td><td align="center">80.91</td><td align="center">86.76</td><td align="center">80.41</td><td align="center">74.65</td> </tr> <tr> <td align="left">DALL-E 3</td><td align="center">-</td><td align="center">90.97</td><td align="center">89.61</td><td align="center">88.39</td><td align="center">90.58</td><td align="center">89.83</td><td align="center">83.50</td> </tr> <tr> <td align="left">SD3-Medium</td><td align="center">2B</td><td align="center">87.90</td><td align="center">91.01</td><td align="center">88.83</td><td align="center">80.70</td><td align="center">88.68</td><td align="center">84.08</td> </tr> <tr> <td align="left">FLUX.1-dev</td><td align="center">12B</td><td align="center">74.35</td><td align="center">90.00</td><td align="center">88.96</td><td align="center">90.87</td><td align="center">88.33</td><td align="center">83.84</td> </tr> <tr> <td align="left">Qwen-Image</td><td align="center">20B</td><td align="center">91.32</td><td align="center">91.56</td><td align="center">92.02</td><td align="center">94.31</td><td align="center">92.73</td><td align="center">88.32</td> </tr> <tr> <td align="center" colspan="8"><i>Unified Models</i></td> </tr> <tr> <td align="left">Janus-Pro-7B</td><td align="center">7B</td><td align="center">86.90</td><td align="center">88.90</td><td align="center">89.40</td><td align="center">89.32</td><td align="center">89.48</td><td align="center">84.19</td> </tr> <tr> <td align="left">OmniGen2</td><td align="center">4B</td><td align="center">88.81</td><td align="center">88.83</td><td align="center">90.18</td><td align="center">89.37</td><td align="center">90.27</td><td align="center">83.57</td> </tr> <tr> <td align="left">Show-o2</td><td align="center">7B</td><td align="center">89.00</td><td align="center"><b>91.78</b></td><td align="center">89.96</td><td align="center">91.81</td><td align="center"><b>91.64</b></td><td align="center">86.14</td> </tr> <tr> <td align="left">BAGEL<sup>†</sup></td><td align="center">7B</td><td align="center">88.94</td><td align="center">90.37</td><td align="center"><u>91.29</u></td><td align="center">90.82</td><td align="center">88.67</td><td align="center">85.07</td> </tr> <tr> <td align="left">InternVL-U</td><td align="center">1.7B</td><td align="center"><u>90.39</u></td><td align="center">90.78</td><td align="center">90.68</td><td align="center">90.29</td><td align="center">88.77</td><td align="center">85.18</td> </tr> <tr> <td align="left">TUNA</td><td align="center">7B</td><td align="center"><b>90.42</b></td><td align="center"><u>91.68</u></td><td align="center">90.94</td><td align="center"><u>91.87</u></td><td align="center"><u>90.73</u></td><td align="center"><b>86.76</b></td> </tr> <tr> <td align="left">TUNA-2</td><td align="center">7B</td><td align="center">89.50</td><td align="center">91.40</td><td align="center"><b>92.07</b></td><td align="center">91.91</td><td align="center">88.81</td><td align="center"><u>86.54</u></td> </tr> <tr bgcolor="#f4e6ff"> <td align="left">🌟 <b>Lance (Ours)</b></td><td align="center"><b>3B</b></td><td align="center"><b>83.89</b></td><td align="center"><b>91.07</b></td><td align="center"><b>89.36</b></td><td align="center"><b>93.38</b></td><td align="center"><b>80.80</b></td><td align="center"><b>84.67</b></td> </tr> </tbody> </table> </div> <p align="center"><em><sup>†</sup> indicates methods that use LLM rewriters for prompt rewriting before generation.</em></p>GenEval Evaluation
<div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center"># Params.</th> <th align="center">1-Obj.</th> <th align="center">2-Obj.</th> <th align="center">Count</th> <th align="center">Colors</th> <th align="center">Position</th> <th align="center">Attr.</th> <th align="center">Overall</th> </tr> </thead> <tbody> <tr> <td align="center" colspan="9"><i>Generation-only Models</i></td> </tr> <tr> <td align="left">SDXL</td><td align="center">3.5B</td><td align="center">0.98</td><td align="center">0.74</td><td align="center">0.39</td><td align="center">0.85</td><td align="center">0.15</td><td align="center">0.23</td><td align="center">0.55</td> </tr> <tr> <td align="left">DALL-E 3</td><td align="center">-</td><td align="center">0.96</td><td align="center">0.87</td><td align="center">0.47</td><td align="center">0.83</td><td align="center">0.43</td><td align="center">0.45</td><td align="center">0.67</td> </tr> <tr> <td align="left">SD3-Medium</td><td align="center">2B</td><td align="center">0.99</td><td align="center">0.94</td><td align="center">0.72</td><td align="center">0.89</td><td align="center">0.33</td><td align="center">0.60</td><td align="center">0.74</td> </tr> <tr> <td align="left">FLUX.1-dev</td><td align="center">12B</td><td align="center">0.98</td><td align="center">0.93</td><td align="center">0.75</td><td align="center">0.93</td><td align="center">0.68</td><td align="center">0.65</td><td align="center">0.82</td> </tr> <tr> <td align="left">Qwen-Image</td><td align="center">20B</td><td align="center">0.99</td><td align="center">0.92</td><td align="center">0.89</td><td align="center">0.88</td><td align="center">0.76</td><td align="center">0.77</td><td align="center">0.87</td> </tr> <tr> <td align="center" colspan="9"><i>Unified Models</i></td> </tr> <tr> <td align="left">Janus-Pro-7B</td><td align="center">7B</td><td align="center"><u>0.99</u></td><td align="center">0.89</td><td align="center">0.59</td><td align="center">0.90</td><td align="center">0.79</td><td align="center">0.66</td><td align="center">0.80</td> </tr> <tr> <td align="left">OmniGen2</td><td align="center">4B</td><td align="center"><b>1.00</b></td><td align="center">0.95</td><td align="center">0.64</td><td align="center">0.88</td><td align="center">0.55</td><td align="center">0.76</td><td align="center">0.80</td> </tr> <tr> <td align="left">Show-o2</td><td align="center">7B</td><td align="center"><b>1.00</b></td><td align="center">0.87</td><td align="center">0.58</td><td align="center">0.92</td><td align="center">0.52</td><td align="center">0.62</td><td align="center">0.76</td> </tr> <tr> <td align="left">BAGEL<sup>†</sup></td><td align="center">7B</td><td align="center">0.98</td><td align="center">0.95</td><td align="center"><b>0.84</b></td><td align="center"><u>0.95</u></td><td align="center">0.78</td><td align="center">0.77</td><td align="center">0.88</td> </tr> <tr> <td align="left">Mogao</td><td align="center">7B</td><td align="center"><b>1.00</b></td><td align="center"><b>0.97</b></td><td align="center"><u>0.83</u></td><td align="center">0.93</td><td align="center">0.84</td><td align="center">0.80</td><td align="center"><u>0.89</u></td> </tr> <tr> <td align="left">InternVL-U</td><td align="center">1.7B</td><td align="center"><u>0.99</u></td><td align="center">0.94</td><td align="center">0.74</td><td align="center">0.91</td><td align="center">0.77</td><td align="center">0.74</td><td align="center">0.85</td> </tr> <tr> <td align="left">TUNA</td><td align="center">7B</td><td align="center"><b>1.00</b></td><td align="center"><b>0.97</b></td><td align="center">0.81</td><td align="center">0.91</td><td align="center"><b>0.88</b></td><td align="center"><b>0.83</b></td><td align="center"><b>0.90</b></td> </tr> <tr> <td align="left">TUNA-2</td><td align="center">7B</td><td align="center"><u>0.99</u></td><td align="center"><u>0.96</u></td><td align="center">0.80</td><td align="center">0.91</td><td align="center">0.84</td><td align="center">0.76</td><td align="center">0.87</td> </tr> <tr bgcolor="#f4e6ff"> <td align="left">🌟 <b>Lance (Ours)</b></td><td align="center"><b>3B</b></td><td align="center"><b>1.00</b></td><td align="center"><b>0.94</b></td><td align="center"><b>0.84</b></td><td align="center"><b>0.97</b></td><td align="center"><b>0.87</b></td><td align="center"><b>0.81</b></td><td align="center"><b>0.90</b></td> </tr> </tbody> </table> </div> <p align="center"><em><sup>†</sup> indicates methods that use LLM rewriters for prompt rewriting before generation.</em></p>GEdit-Bench Evaluation
<div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center"># Params.</th> <th align="center">BC</th> <th align="center">CA</th> <th align="center">MM</th> <th align="center">MC</th> <th align="center">PB</th> <th align="center">ST</th> <th align="center">SA</th> <th align="center">SR</th> <th align="center">SRp</th> <th align="center">TM</th> <th align="center">TT</th> <th align="center">Avg/G_O</th> </tr> </thead> <tbody> <tr> <td align="center" colspan="14"><i>Generation-only Models</i></td> </tr> <tr> <td align="left">Gemini 2.0</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">-</td><td align="center">6.32</td> </tr> <tr> <td align="left">GPT Image 1</td><td align="center">-</td><td align="center">6.96</td><td align="center">6.85</td><td align="center">7.10</td><td align="center">5.41</td><td align="center">6.74</td><td align="center">7.44</td><td align="center">7.51</td><td align="center">8.73</td><td align="center">8.55</td><td align="center">8.45</td><td align="center">8.69</td><td align="center">7.49</td> </tr> <tr> <td align="left">Qwen-Image-Edit</td><td align="center">20B</td><td align="center">8.23</td><td align="center">8.30</td><td align="center">7.33</td><td align="center">8.05</td><td align="center">7.49</td><td align="center">6.74</td><td align="center">8.57</td><td align="center">8.09</td><td align="center">8.29</td><td align="center">8.48</td><td align="center">8.50</td><td align="center">8.01</td> </tr> <tr> <td align="center" colspan="14"><i>Unified Models</i></td> </tr> <tr> <td align="left">Lumina-DiMOO</td><td align="center">8B</td><td align="center">3.43</td><td align="center">4.27</td><td align="center">3.08</td><td align="center">2.77</td><td align="center">4.74</td><td align="center">5.19</td><td align="center">4.44</td><td align="center">3.80</td><td align="center">4.38</td><td align="center">2.68</td><td align="center">4.20</td><td align="center">3.91</td> </tr> <tr> <td align="left">Ovis-U1</td><td align="center">1.2B</td><td align="center"><u>7.49</u></td><td align="center">6.88</td><td align="center">6.21</td><td align="center">4.79</td><td align="center">5.98</td><td align="center"><u>6.46</u></td><td align="center">7.49</td><td align="center"><u>7.25</u></td><td align="center"><u>7.27</u></td><td align="center">4.48</td><td align="center">6.31</td><td align="center">6.42</td> </tr> <tr> <td align="left">BAGEL</td><td align="center">7B</td><td align="center">7.32</td><td align="center">6.91</td><td align="center">6.38</td><td align="center">4.75</td><td align="center">4.57</td><td align="center">6.15</td><td align="center"><b>7.90</b></td><td align="center">7.16</td><td align="center">7.02</td><td align="center"><u>7.32</u></td><td align="center">6.22</td><td align="center">6.52</td> </tr> <tr> <td align="left">InternVL-U</td><td align="center">1.7B</td><td align="center">7.08</td><td align="center">7.05</td><td align="center">6.38</td><td align="center"><u>7.02</u></td><td align="center"><u>6.03</u></td><td align="center">6.27</td><td align="center">7.13</td><td align="center">6.55</td><td align="center">6.33</td><td align="center">6.59</td><td align="center"><u>6.85</u></td><td align="center">6.66</td> </tr> <tr> <td align="left">InternVL-U (w/ CoT)</td><td align="center">1.7B</td><td align="center">7.05</td><td align="center"><b>7.87</b></td><td align="center"><u>6.50</u></td><td align="center">6.99</td><td align="center">5.77</td><td align="center">6.10</td><td align="center">7.33</td><td align="center">7.16</td><td align="center">7.12</td><td align="center"><b>7.36</b></td><td align="center">6.46</td><td align="center"><u>6.88</u></td> </tr> <tr bgcolor="#f4e6ff"> <td align="left">🌟 <b>Lance (Ours)</b></td><td align="center"><b>3B</b></td><td align="center"><b>7.73</b></td><td align="center"><u>7.74</u></td><td align="center"><b>7.28</b></td><td align="center"><b>7.83</b></td><td align="center"><b>7.50</b></td><td align="center"><b>7.03</b></td><td align="center"><u>7.64</u></td><td align="center"><b>7.85</b></td><td align="center"><b>7.71</b></td><td align="center">4.46</td><td align="center"><b>7.57</b></td><td align="center"><b>7.30</b></td> </tr> </tbody> </table> </div>VBench Evaluation (Video Generation)
<div align="center"> <table align="center"> <thead> <tr> <th align="left">Type</th> <th align="left">Model</th> <th align="center"># Params.</th> <th align="center">Total Score ↑</th> </tr> </thead> <tbody> <tr> <td align="center" rowspan="12"><i>Gen. Only</i></td> <td align="left">ModelScope</td><td align="center">1.7B</td><td align="center">75.75</td> </tr> <tr> <td align="left">LaVie</td><td align="center">3B</td><td align="center">77.08</td> </tr> <tr> <td align="left">Show-1</td><td align="center">6B</td><td align="center">78.93</td> </tr> <tr> <td align="left">AnimateDiff-V2</td><td align="center">-</td><td align="center">80.27</td> </tr> <tr> <td align="left">VideoCrafter-2.0</td><td align="center">-</td><td align="center">80.44</td> </tr> <tr> <td align="left">CogVideoX</td><td align="center">5B</td><td align="center">81.61</td> </tr> <tr> <td align="left">Kling</td><td align="center">-</td><td align="center">81.85</td> </tr> <tr> <td align="left">Open-Sora-2.0</td><td align="center">-</td><td align="center">81.71</td> </tr> <tr> <td align="left">Gen-3</td><td align="center">-</td><td align="center">82.32</td> </tr> <tr> <td align="left">Step-Video-T2V</td><td align="center">30B</td><td align="center">81.83</td> </tr> <tr> <td align="left">Hunyuan Video</td><td align="center">-</td><td align="center">83.43</td> </tr> <tr> <td align="left">Wan2.1-T2V</td><td align="center">14B</td><td align="center">83.69</td> </tr> <tr> <td align="center" rowspan="6"><i>Unified</i></td> <td align="left">HaproOmni</td><td align="center">7B</td><td align="center">78.10</td> </tr> <tr> <td align="left">Emu3</td><td align="center">8B</td><td align="center">80.96</td> </tr> <tr> <td align="left">VILA-U</td><td align="center">7B</td><td align="center">74.01</td> </tr> <tr> <td align="left">Show-o2</td><td align="center">2B</td><td align="center">81.34</td> </tr> <tr> <td align="left">TUNA</td><td align="center">1.5B</td><td align="center"><u>84.06</u></td> </tr> <tr bgcolor="#f4e6ff"> <td align="left">🌟 <b>Lance (Ours)</b></td><td align="center"><b>3B</b></td><td align="center"><b>85.11</b></td> </tr> </tbody> </table> </div>Running Benchmarks
Ready-to-run benchmark scripts are provided under benchmarks/:
| Benchmark | Modality | Script |
|---|---|---|
| GenEVAL (image gen) | Image | benchmarks/image_gen/GenEVAL/sample_GenEVAL.sh |
| DPG (image gen) | Image | benchmarks/image_gen/DPG/sample_DPG.sh |
| GEdit (image edit) | Image | benchmarks/image_gen/GEdit/sample_GEdit.sh |
| VBench (video gen) | Video | benchmarks/video_gen/Vbench/sample_vbench.sh |
📄 License
Copyright 2025 Bytedance Ltd. and/or its affiliates.
💖 Citation
If you find Lance useful for your project or research, welcome to 🌟 this repo and cite our work using the following BibTeX:
@misc{lance2026,
title = {Lance: Unified Multimodal Modeling by Multi-Task Synergy},
author = {Fengyi Fu and Mengqi Huang and Shaojin Wu and Yunsheng Jiang and Yufei Huo and Jianzhu Guo and Hao Li and Yinghang Song and Fei Ding and Qian He and Zheren Fu and Zhendong Mao and Yongdong Zhang},
year = {2026},
note = {Manuscript}
}
📞 Contact
For questions, issues, or collaborations, please contact Mengqi Huang and Jianzhu Guo.
- Task
- Any-to-Any
- Library
- Lance
- License
- apache-2.0
- Created
- May 18, 2026
- Updated
- May 18, 2026