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
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
Image Editing
Image Understanding
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.
Question:
What percentage of respondents want better border security?
Response:
29%
Question:
What is the license plate number of the car?
Response:
The license plate number of the car is BX62 BFY.
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.
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.
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
).
†
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}
}
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