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Lance

A 3B-active-parameter native unified multimodal model for image and video understanding, generation, and editing.

From bytedanceยทUpdated June 14, 2026ยทView on GitHubยท

**Lance** is A 3B-active-parameter native unified multimodal model for image and video understanding, generation, and editing. The project is written primarily in Python, distributed under the Apache License 2.0 license, first published in 2026. It has gained significant community traction with 1,202 stars and 79 forks on GitHub. Key topics include: image-editing, image-generation, image-understanding, unified-multimodal-models, video-generation.

<div align="center"> <img src="assets/logo/lance-logo.webp" alt="Lance logo" width="300"> <h1 align="center"><sup>Lance: Unified Multimodal Modeling by Multi-Task Synergy</sup></h1> <p> <strong> <a href="https://scholar.google.com.hk/citations?user=FXxoQlsAAAAJ&hl=zh-CN&oi=ao" style="text-decoration: none; color: inherit;">Fengyi Fu</a><sup>*</sup>, <a href="https://corleone-huang.github.io/" style="text-decoration: none; color: inherit;">Mengqi Huang</a><sup>*,โœ‰</sup>, <a href="https://scholar.google.com.hk/citations?user=9ER6nVkAAAAJ&hl=zh-CN&oi=ao" style="text-decoration: none; color: inherit;">Shaojin Wu</a><sup>*</sup>, Yunsheng Jiang<sup>*</sup>, Yufei Huo, <a href="https://guojianzhu.com/" style="text-decoration: none; color: inherit;">Jianzhu Guo</a><sup>โœ‰,ยง</sup> </strong><br> Hao Li, Yinghang Song, Fei Ding, Qian He, Zheren Fu, Zhendong Mao, Yongdong Zhang <br> <em>ByteDance</em> <br> <sup>*</sup> Equal contribution &nbsp;&nbsp; <sup>โœ‰</sup> Corresponding authors &nbsp;&nbsp; <sup>ยง</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="http://arxiv.org/abs/2605.18678" 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://huggingface.co/bytedance-research/Lance" style="text-decoration: none; margin: 0 8px;"><img src="https://img.shields.io/badge/Model-HuggingFace-yellow?style=flat&logo=huggingface" alt="Model"></a> <a href="https://huggingface.co/spaces/bytedance-research/Lance" style="text-decoration: none; margin: 0 8px;"><img src="https://img.shields.io/badge/Demo-HuggingFace-40bfe6?style=flat&logo=huggingface" alt="Demo"></a> <br> English | <a href="./README_zh.md"><ins>็ฎ€ไฝ“ไธญๆ–‡</ins></a> </p> </div>

Note: Lance is a research project rather than a polished product model. The released checkpoint was trained with up to 128 A100 GPUs, with training conducted up to 768x768 image generation and 480p, 12 FPS video generation. Our goal is to share a research artifact for studying unified image/video understanding, generation, and editing under a relatively small model and limited compute budget. Output quality may vary across prompts, resolutions, duration, motion complexity, and editing scenarios, and we see further opportunities to improve the post-training recipe. We appreciate constructive feedback from the community as we continue improving the project.

๐Ÿ”ฅ Updates

  • 2026/06/03: ๐Ÿš€ Lance is now supported in vLLM-Omni. See the recipe!
  • 2026/05/29: ๐Ÿ’ช Added support for Image-to-Video generation. More to see!
  • 2026/05/26: ๐ŸŽจ The Gradio interface now supports image and video generation, editing, and understanding. Try it out!
  • 2026/05/25: โœจ The Hugging Face Space is now live, thanks to the HF team!
  • 2026/05/19: ๐Ÿค— The technical report is now available on arXiv.
  • 2026/05/18: ๐Ÿ”ฅ We launched the project homepage and released the initial inference code and model weights on GitHub and Hugging Face.

๐ŸŒŸ Highlights

Lance is a 3B 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 achieves competitive performance across image generation, image editing, and video generation benchmarks.
  • Training from scratch. Lance is trained from scratch with a staged multi-task recipe and within a budget of up to 128 A100 GPUs.

We are actively updating and improving this repository. If you find any bugs or have suggestions, please feel free to open an issue or submit a pull request (PR) ๐Ÿ’–.

<div align="center"> <img src="assets/benchmarks/benchmark-overview.png" alt="Lance benchmark overview across image generation, image editing, video generation, and video understanding" width="980"> </div>

๐Ÿ“… Roadmap

  • Release the fine-tuning code.

๐ŸŽจ Demo

<details> <summary><strong>Show demo results</strong></summary> <div align="center"> <strong>๐Ÿ”ฅ We recommend visiting our <a href="https://lance-project.github.io/">homepage</a> for more visual results. ๐Ÿ”ฅ</strong> </div> <h3 align="center">Text-to-Video</h3> <table align="center"> <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> <h3 align="center">Video Editing</h3> <table align="center"> <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> <h3 align="center">Multi-turn Consistency Editing</h3> <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> <h3 align="center">Intelligent Video Generation</h3> <table align="center"> <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> </details>

๐Ÿš€ Installation

  • Software: Python 3.10+, CUDA 12.4+ (required)
  • Hardware: A GPU with at least 40GB VRAM is required for inference

We have tested the following dependency combinations on NVIDIA A100:

  • PyTorch 2.8.0 + cu126 + flash-attn 2.8.3
  • PyTorch 2.5.1 + cu124 + flash-attn 2.6.3

The default installation commands use the PyTorch 2.8.0 + cu126 setup. For other GPU models, please choose and validate a PyTorch build and a matching flash-attn version according to your driver, CUDA runtime, Python version, and GPU architecture.

Installation Steps

First, clone the repository:

bash
git clone https://github.com/bytedance/Lance.git
cd Lance

Then, set up the environment:

bash
conda create -n Lance python=3.11 -y
conda activate Lance
pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt
pip install flash-attn==2.8.3 --no-build-isolation

Note: If installing flash-attn from source fails, you can install a prebuilt wheel instead. The wheelhouse below is from a third-party repository and is provided for reference only; please verify that any wheel you install matches your Python, PyTorch and CUDA versions.

bash
pip install --no-cache-dir --no-deps --force-reinstall \
"https://huggingface.co/strangertoolshf/flash_attention_2_wheelhouse/resolve/main/wheelhouse-flash_attn-2.8.3/linux_x86_64/torch2.8/cu12/abiTRUE/cp311/flash_attn-2.8.3+cu12torch2.8cxx11abiTRUE-cp311-cp311-linux_x86_64.whl"

Then, download the model weights from Lance-3B on Hugging Face and place them in the downloads/ directory:

bash
from huggingface_hub import snapshot_download

save_dir = "./downloads/"
repo_id = "bytedance-research/Lance"
cache_dir = save_dir + "/cache"

snapshot_download(cache_dir=cache_dir,
  local_dir=save_dir,
  repo_id=repo_id,
  local_dir_use_symlinks=False,
  resume_download=True,
  allow_patterns=["*.json", "*.safetensors", "*.bin", "*.py", "*.md", "*.txt","*.pth",],
)

๐Ÿ“š Usage

Inference

Basic Usage

bash
bash inference_lance.sh
  • Before running, please configure the inference parameters at the top of inference_lance.sh.
  • Supported tasks: t2i, t2v, i2v, image_edit, video_edit, x2t_image, and x2t_video. You can modify TASK_DEFAULT_CONFIGS in inference_lance.py to customize the default data samples for each task.
  • Note: For all tasks, we recommend following the prompt format used in the provided examples when writing input prompts, as this typically leads to better generation quality.

Task Examples

Text-to-Video
bash
bash inference_lance.sh \
  --TASK_NAME t2v \
  --MODEL_PATH downloads/Lance_3B_Video \
  --RESOLUTION video_480p \
  --NUM_FRAMES 121 \
  --VIDEO_HEIGHT 480 \
  --VIDEO_WIDTH 848 \
  --SAVE_PATH_GEN results/t2v
Image-to-Video
bash
bash inference_lance.sh \
  --TASK_NAME i2v \
  --MODEL_PATH downloads/Lance_3B_Video \
  --RESOLUTION video_480p \
  --NUM_FRAMES 61 \
  --VIDEO_HEIGHT 480 \
  --VIDEO_WIDTH 848 \
  --SAVE_PATH_GEN results/i2v

Optional parameters for video generation task examples:

  • --ENHANCE_PROMPT true: enable prompt rewrite for T2V/I2V. Prompt enhancement generally improves generation quality. This option requires openai==2.26.0, which is already listed in requirements.txt; if you did not install from requirements.txt, run pip install openai==2.26.0 first. Before enabling it, set API_KEY, MODEL_NAME, and BASE_URL in common/utils/caption_rewrite.py. If no valid rewrite config is provided there, prompt rewrite is skipped; in that case, we recommend writing prompts in the style of the provided examples.
Text-to-Image
bash
bash inference_lance.sh \
  --TASK_NAME t2i \
  --MODEL_PATH downloads/Lance_3B \
  --RESOLUTION image_768res \
  --VIDEO_HEIGHT 768 \
  --VIDEO_WIDTH 768 \
  --SAVE_PATH_GEN results/t2i
Video Editing
bash
bash inference_lance.sh \
  --TASK_NAME video_edit \
  --MODEL_PATH downloads/Lance_3B_Video \
  --RESOLUTION video_480p \
  --SAVE_PATH_GEN results/video_edit
Image Editing
bash
bash inference_lance.sh \
  --TASK_NAME image_edit \
  --MODEL_PATH downloads/Lance_3B \
  --RESOLUTION image_768res \
  --SAVE_PATH_GEN results/image_edit
Video Understanding
bash
bash inference_lance.sh \
  --TASK_NAME x2t_video \
  --MODEL_PATH downloads/Lance_3B_Video \
  --RESOLUTION video_480p \
  --NUM_FRAMES 50 \
  --SAVE_PATH_GEN results/x2t_video
Image Understanding
bash
bash inference_lance.sh \
  --TASK_NAME x2t_image \
  --MODEL_PATH downloads/Lance_3B \
  --RESOLUTION image_768res \
  --SAVE_PATH_GEN results/x2t_image

Optional parameters for all task examples:

  • --CONFIG_PATH path/to/config.json: use a custom validation JSON/JSONL file instead of the task default example config.
<details> <summary><strong>Show task and parameter reference</strong></summary>

Available Tasks

Task NameDescriptionExample JSON
t2vText-to-Video generationconfig/examples/t2v_example.json
t2iText-to-Image generationconfig/examples/t2i_example.json
i2vImage-to-Video generationconfig/examples/i2v_example.json
image_editImage editingconfig/examples/image_edit_example.json
video_editVideo editingconfig/examples/video_edit_example.json
x2t_imageImage understandingconfig/examples/x2t_image_example.json
x2t_videoVideo understandingconfig/examples/x2t_video_example.json

For understanding examples:

  • config/examples/x2t_image_example.json: image understanding examples for visual question answering, reasoning and image captioning.
  • 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:

ParameterDefault ValueDescription
MODEL_PATH"downloads/Lance_3B"Path to the downloaded Lance model weights (Lance_3B or Lance_3B_Video).
NUM_GPUS1Number of GPUs to use for inference.
VALIDATION_NUM_TIMESTEPS30Number of denoising steps (e.g., 30 or 50).
VALIDATION_TIMESTEP_SHIFT3.5Timestep shift parameter for flow matching scheduling.
CFG_TEXT_SCALE4.0Classifier-Free Guidance (CFG) scale for text conditioning.
VALIDATION_DATA_SEED42Random seed for generation reproducibility.
NUM_FRAMES50Number of frames for video generation (Max: 121). Unused for image tasks.
VIDEO_HEIGHT / VIDEO_WIDTH768Spatial resolution. Unused for editing tasks (determined by input image/video).
RESOLUTION"video_480p"Base resolution preset (image_768res or video_480p).
CONFIG_PATH""Optional path to a custom validation JSON/JSONL file. When empty, the task default example config is used.
ENHANCE_PROMPTfalseOptional T2V/I2V prompt rewrite switch. T2V uses text-only rewrite; I2V uses text plus the input image. Prompt enhancement generally improves generation quality. This option requires openai==2.26.0; it is included in requirements.txt, or install it manually with pip install openai==2.26.0. Configure API_KEY, MODEL_NAME, and BASE_URL in common/utils/caption_rewrite.py before setting this to true; without a valid rewrite config, we recommend writing prompts in the style of the provided examples.
</details>

๐Ÿ–ฅ๏ธ Gradio

You can launch the local Gradio demo for video/image generation, editing, and understanding:

bash
python lance_gradio.py --server-name 0.0.0.0 --server-port 7860

Benchmarks

<details> <summary><strong>DPG-Bench Evaluation</strong></summary> <div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center">#&nbsp;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>โ€ </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> <td align="left">๐ŸŒŸ <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>โ€ </sup> indicates methods that use LLM rewriters for prompt rewriting before generation.</em></p> </details> <details> <summary><strong>GenEval Evaluation</strong></summary> <div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center">#&nbsp;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>โ€ </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> <td align="left">๐ŸŒŸ <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>โ€ </sup> indicates methods that use LLM rewriters for prompt rewriting before generation.</em></p> </details> <details> <summary><strong>GEdit-Bench Evaluation</strong></summary> <div align="center"> <table align="center"> <thead> <tr> <th align="left">Models</th> <th align="center">#&nbsp;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> <td align="left">๐ŸŒŸ <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> </details> <details> <summary><strong>VBench Evaluation (Video Generation)</strong></summary> <div align="center"> <table align="center"> <thead> <tr> <th align="left">Type</th> <th align="left">Model</th> <th align="center">#&nbsp;Params.</th> <th align="center">Total Score โ†‘</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> <td align="left">๐ŸŒŸ <b>Lance (Ours)</b></td><td align="center"><b>3B</b></td><td align="center"><b>85.11</b></td> </tr> </tbody> </table> </div> </details>

Running Benchmarks

Ready-to-run benchmark scripts are provided under benchmarks/:

BenchmarkModalityScript
GenEVAL (image gen)Imagebenchmarks/image_gen/GenEVAL/sample_GenEVAL.sh
DPG (image gen)Imagebenchmarks/image_gen/DPG/sample_DPG.sh
GEdit (image edit)Imagebenchmarks/image_gen/GEdit/sample_GEdit.sh
VBench (video gen)Videobenchmarks/video_gen/Vbench/sample_vbench.sh

๐Ÿ“„ License

Copyright 2025 ByteDance Ltd. and/or its affiliates.

๐Ÿ™ Acknowledgements

We would like to thank the contributors of BAGEL, Qwen2.5-VL-3B-Instruct, and Wan2.2 for their open research and contributions.

๐Ÿ’– Citation

If you find Lance useful for your project or research, welcome to ๐ŸŒŸ this repo and cite our work using the following BibTeX:

bibtex
@misc{fu2026lanceunifiedmultimodalmodeling,
      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 Hao Li and Yinghang Song and Fei Ding and Jianzhu Guo and Qian He and Zheren Fu and Zhendong Mao and Yongdong Zhang},
      year          = {2026},
      eprint        = {2605.18678},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV},
      url           = {https://arxiv.org/abs/2605.18678},
}

๐Ÿ“ž Contact

For questions, issues, or collaborations, please contact Mengqi Huang and Jianzhu Guo.

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