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bytedance/lance

★ 637PythonAudience · researcherComplexity · 5/5Setup · hard

TLDR

ByteDance research release of a 3B-active-parameter unified multimodal model that handles image and video understanding, generation, and editing within one set of weights.

Mindmap

mindmap
  root((Lance))
    Inputs
      Text prompts
      Images
      Video clips
    Outputs
      Generated images
      Generated video
      Edited media
      VQA answers
    Use Cases
      Text to video demos
      Multi-turn editing
      Video question answering
    Tech Stack
      PyTorch
      Python
      ViT
      VAE
      HuggingFace
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Code map

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Things people build with this

USE CASE 1

Reproduce benchmark numbers for a unified image and video model at 3B scale

USE CASE 2

Run text-to-video generation locally from the published HuggingFace weights

USE CASE 3

Apply multi-turn edits to a video clip while keeping subject consistency

USE CASE 4

Run video question answering on short clips with multiple choice options

Tech stack

PythonPyTorchHuggingFaceViTVAECUDA

Getting it running

Difficulty · hard Time to first run · 1day+

README ships demo galleries but no install or inference instructions, need to dig into the HuggingFace page and likely have multi-GPU A100 hardware.

In plain English

Lance is a research release from ByteDance that tries to put four different vision tasks into one model: understanding what is in an image or video, generating new images and video from text, and editing existing images and video. The README calls it a unified multimodal model, meaning the same set of weights handles all of these jobs instead of needing a separate model for each. The headline number is the size. Lance has 3 billion active parameters, which is small for a model that claims to compete on image generation, image editing, and video generation benchmarks at the same time. The authors say the transformer backbone was trained from scratch, with only the vision encoder (ViT) and the autoencoder (VAE) reused from existing work. Training was done on a 128 A100 GPU budget, which the paper presents as modest for this kind of multi-task model. The repository links to a project homepage, an arXiv paper, and a Hugging Face page where the model weights are published. Most of the README space is a gallery of demo clips: text prompts turned into short videos, edits applied to existing video clips, multi-turn editing where consistency is kept across rounds, an intelligent video generation section, and video question answering examples where the model picks the right answer from multiple choice options about a clip. What the README does not include in the part shown is concrete install instructions, training code layout, or hardware requirements for running it locally. Readers who want to actually use Lance would need to follow the Hugging Face model page or read further into the repo.

Copy-paste prompts

Prompt 1
Walk me through how Lance routes a text prompt to image generation vs video generation in one backbone
Prompt 2
Show me how to load the Lance weights from HuggingFace and run text-to-video inference
Prompt 3
Reproduce Lance's multi-turn video editing demo using the released checkpoint
Prompt 4
Compare Lance's 3B unified architecture to separate-task models like SDXL plus a video model
Prompt 5
Set up a single A100 to run Lance inference and estimate VRAM needed per task
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