Show HN: Lance – image/video generation and understanding in one model

原始链接: https://github.com/bytedance/Lance

Hacker Newsnew | past | comments | ask | show | jobs | submitloginShow HN: Lance – image/video generation and understanding in one model (github.com/bytedance)13 points by cleardusk 1 hour ago | hide | past | favorite | 2 commentsThe model has 3B active parameters. We put the code, homepage, paper and model links here:- Code: https://github.com/bytedance/Lance- Homepage: https://lance-project.github.io/- Paper: https://arxiv.org/abs/2605.18678- Model: https://huggingface.co/bytedance-research/Lancep.s. Lance is a research project, not a polished product. The model was trained using fewer than 128 GPUs. help asadm 18 minutes ago | next [–] last dance for lance vance!replyTsarp 26 minutes ago | prev [–] Nice work. Wish they had picked another name given how popular lance/lancedb is.reply Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact Search:

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

English | 简体中文

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 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 (except for the ViT and VAE encoders; the transformer backbone is trained entirely from scratch) within a 128-A100-GPU budget.

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) 💖.

Multi-turn Consistency Editing

Intelligent Video Generation

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.

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%

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.

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

Please download all necessary model checkpoints from Lance-3B on Hugging Face and place them in the downloads/ directory.

We provide a unified command-line interface for all generation / editing / understanding tasks:

Option 1: Configure and Run the Unified Script

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.
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.

Option 2: Configure and Run the Unified Script

We provide task-specific one-click commands for different generation, editing, and understanding tasks.

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

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

bash inference_lance.sh \
  --TASK_NAME video_edit \
  --MODEL_PATH downloads/Lance_3B_Video \
  --RESOLUTION video_480p \
  --SAVE_PATH_GEN results/video_edit

bash inference_lance.sh \
  --TASK_NAME image_edit \
  --MODEL_PATH downloads/Lance_3B \
  --RESOLUTION image_768res \
  --SAVE_PATH_GEN results/image_edit

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

bash inference_lance.sh \
  --TASK_NAME x2t_image \
  --MODEL_PATH downloads/Lance_3B \
  --RESOLUTION image_768res \
  --SAVE_PATH_GEN results/x2t_image

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.

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 (`Lance_3B` or `Lance_3B_Video`).
`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`).

python lance_gradio_t2v_v2t.py --gpus 0 --server-port 7860

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

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`

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

If you find Lance useful for your project or research, welcome to 🌟 this repo and cite our work using the following 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},
}

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