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Introduction of Lance

Model Details of Lance

Lance logo

Lance: Unified Multimodal Modeling by Multi-Task Synergy

Fengyi Fu * , Mengqi Huang *,✉ , Shaojin Wu * , Yunsheng Jiang * , Yufei Huo , Jianzhu Guo ✉,§
Hao Li, Yinghang Song, Fei Ding, Qian He, Zheren Fu, Zhendong Mao, Yongdong Zhang
ByteDance
* Equal contribution ✉ Corresponding authors § Project lead

Homepage arXiv Github
English | 简体中文

🌟 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.
Lance benchmark overview across image generation, image editing, video generation, and video understanding
🎨 Demo
Text-to-Video
Video Editing
Multi-turn Consistency Editing
Intelligent Video Generation
Video Understanding

Question: How many times did the person launch objects on the table? Options: (A) 3 (B) 2 (C) 4

Response: (A) 3

Question: The person makes sets of repeated actions. How many distinct repeated actions did the person do? Options: (A) 2 (B) 3 (C) 4

Response: (A) 2

Question: 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.

Response: (A) Down and to the right.

Question: 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.

Response: (B) Man grabs an object through a phone screen.

Question: Offer a succinct account of the culinary process shown in this video.

Response: Add tomato puree and mix it well with chicken pieces.

Question: Provide a detailed description of the given video, capturing its key moments.

Response: 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.

Text-to-Image Generation
Lance text-to-image generation examples
Image Editing
Lance image editing examples
Image Understanding
Pie chart reasoning case

Question: Is the largest segment greater than sum of all the other segments?

Response: Yes, the largest segment in the pie chart is the blue one, which is the most populous segment.

Survey chart about border security priorities

Question: What percentage of respondents want better border security?

Response: 29%

License plate recognition case

Question: What is the license plate number of the car?

Response: The license plate number of the car is BX62 BFY.

Market research chart reasoning case

Question: According to the data from the proprietary market research, how much amount was spent on the promotional meetings and events during 1998?

Response: 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.

Colosseum description case

Question: What is the appearance of the Colosseum in Rome, Italy?

Response: 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.

Total solar eclipse case

Question: How does a total solar eclipse look like from Earth?

Response: 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.

🚀 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 , and x2t_video . You can modify TASK_DEFAULT_CONFIGS in inference_lance.py to 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
Models # Params. Global Entity Attribute Relation Other Overall
Generation-only Models
SDXL 3.5B 83.27 82.43 80.91 86.76 80.41 74.65
DALL-E 3 - 90.97 89.61 88.39 90.58 89.83 83.50
SD3-Medium 2B 87.90 91.01 88.83 80.70 88.68 84.08
FLUX.1-dev 12B 74.35 90.00 88.96 90.87 88.33 83.84
Qwen-Image 20B 91.32 91.56 92.02 94.31 92.73 88.32
Unified Models
Janus-Pro-7B 7B 86.90 88.90 89.40 89.32 89.48 84.19
OmniGen2 4B 88.81 88.83 90.18 89.37 90.27 83.57
Show-o2 7B 89.00 91.78 89.96 91.81 91.64 86.14
BAGEL † 7B 88.94 90.37 91.29 90.82 88.67 85.07
InternVL-U 1.7B 90.39 90.78 90.68 90.29 88.77 85.18
TUNA 7B 90.42 91.68 90.94 91.87 90.73 86.76
TUNA-2 7B 89.50 91.40 92.07 91.91 88.81 86.54
🌟 Lance (Ours) 3B 83.89 91.07 89.36 93.38 80.80 84.67

† indicates methods that use LLM rewriters for prompt rewriting before generation.

GenEval Evaluation
Models # Params. 1-Obj. 2-Obj. Count Colors Position Attr. Overall
Generation-only Models
SDXL 3.5B 0.98 0.74 0.39 0.85 0.15 0.23 0.55
DALL-E 3 - 0.96 0.87 0.47 0.83 0.43 0.45 0.67
SD3-Medium 2B 0.99 0.94 0.72 0.89 0.33 0.60 0.74
FLUX.1-dev 12B 0.98 0.93 0.75 0.93 0.68 0.65 0.82
Qwen-Image 20B 0.99 0.92 0.89 0.88 0.76 0.77 0.87
Unified Models
Janus-Pro-7B 7B 0.99 0.89 0.59 0.90 0.79 0.66 0.80
OmniGen2 4B 1.00 0.95 0.64 0.88 0.55 0.76 0.80
Show-o2 7B 1.00 0.87 0.58 0.92 0.52 0.62 0.76
BAGEL † 7B 0.98 0.95 0.84 0.95 0.78 0.77 0.88
Mogao 7B 1.00 0.97 0.83 0.93 0.84 0.80 0.89
InternVL-U 1.7B 0.99 0.94 0.74 0.91 0.77 0.74 0.85
TUNA 7B 1.00 0.97 0.81 0.91 0.88 0.83 0.90
TUNA-2 7B 0.99 0.96 0.80 0.91 0.84 0.76 0.87
🌟 Lance (Ours) 3B 1.00 0.94 0.84 0.97 0.87 0.81 0.90

† indicates methods that use LLM rewriters for prompt rewriting before generation.

GEdit-Bench Evaluation
Models # Params. BC CA MM MC PB ST SA SR SRp TM TT Avg/G_O
Generation-only Models
Gemini 2.0 - - - - - - - - - - - - 6.32
GPT Image 1 - 6.96 6.85 7.10 5.41 6.74 7.44 7.51 8.73 8.55 8.45 8.69 7.49
Qwen-Image-Edit 20B 8.23 8.30 7.33 8.05 7.49 6.74 8.57 8.09 8.29 8.48 8.50 8.01
Unified Models
Lumina-DiMOO 8B 3.43 4.27 3.08 2.77 4.74 5.19 4.44 3.80 4.38 2.68 4.20 3.91
Ovis-U1 1.2B 7.49 6.88 6.21 4.79 5.98 6.46 7.49 7.25 7.27 4.48 6.31 6.42
BAGEL 7B 7.32 6.91 6.38 4.75 4.57 6.15 7.90 7.16 7.02 7.32 6.22 6.52
InternVL-U 1.7B 7.08 7.05 6.38 7.02 6.03 6.27 7.13 6.55 6.33 6.59 6.85 6.66
InternVL-U (w/ CoT) 1.7B 7.05 7.87 6.50 6.99 5.77 6.10 7.33 7.16 7.12 7.36 6.46 6.88
🌟 Lance (Ours) 3B 7.73 7.74 7.28 7.83 7.50 7.03 7.64 7.85 7.71 4.46 7.57 7.30
VBench Evaluation (Video Generation)
Type Model # Params. Total Score ↑
Gen. Only ModelScope 1.7B 75.75
LaVie 3B 77.08
Show-1 6B 78.93
AnimateDiff-V2 - 80.27
VideoCrafter-2.0 - 80.44
CogVideoX 5B 81.61
Kling - 81.85
Open-Sora-2.0 - 81.71
Gen-3 - 82.32
Step-Video-T2V 30B 81.83
Hunyuan Video - 83.43
Wan2.1-T2V 14B 83.69
Unified HaproOmni 7B 78.10
Emu3 8B 80.96
VILA-U 7B 74.01
Show-o2 2B 81.34
TUNA 1.5B 84.06
🌟 Lance (Ours) 3B 85.11
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 .

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https://huggingface.co/eadx/Lance

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