- Category
- AI Tools
- Rank
- No. 623Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- bytedance
- GitHub
- 1.3k stars
- Date
About
A 3B-active-parameter native unified multimodal model from ByteDance for image and video understanding, generation, and editing.
What it does
Lance lets one checkpoint handle seven visual workflows: creating images or videos from text, animating an image, editing existing media, and answering questions about images or videos. Users select a task, supply example-shaped prompt data, and run local inference or the Gradio interface.
Why it's ranked here
Lance is compelling as a research artifact because it puts generation, editing, and understanding behind one model and includes both inference and fine-tuning code. The practical bar is high, however: local inference requires CUDA and at least 40GB of GPU memory, while the authors explicitly warn that output quality varies.
What's good
The task coverage is unusually coherent. Text-to-image, text-to-video, image-to-video, image editing, video editing, and visual question answering share one inference structure. The repository also provides example configurations, downloadable weights, a browser interface, checkpoint handling, and distributed fine-tuning support rather than limiting the release to demonstration media.
Tradeoffs
This is explicitly a research project, not a polished product model. Inference needs Python 3.10 or newer, CUDA 12.4 or newer, and at least 40GB of GPU memory. The tested setup centers on NVIDIA A100 hardware. Results may change with prompts, resolution, duration, motion complexity, and editing scenarios. Remote object storage support is absent from the public training path.
How to use it well
Use Lance when researching one local model across several image and video tasks, especially when you can follow supplied prompt formats and have a large CUDA GPU. Start with the Gradio interface or task examples, then move to scripted inference and fine-tuning. It does not cover lightweight CPU deployment, modest consumer GPUs, polished production guarantees, or remote object storage ingestion.
Technical notes+
README.md documents source installation, Hugging Face checkpoint download, seven task names, CUDA requirements, shell-based inference, and Gradio usage. inference_lance.py maps each task to a model family, example JSON, and output prefix, loads model.safetensors or ema.safetensors, runs generation and understanding branches, and writes structured JSON results. lance_gradio.py initializes Qwen2 language and vision components plus WanVideoVAE, casts the model to bfloat16, and requires CUDA. train/fsdp_utils.py provides FSDP wrapping and full or sharded checkpoint handling. data/tos.py deliberately replaces remote TOS operations with NotImplementedError, directing training toward local parquet data with embedded media bytes.
Observed
- License
- Apache License 2.0 headers appear across the supplied Python source.
- Primary language
- Python.
- Installation surface
- Clone the source repository, create a Conda environment, install pinned requirements and flash-attn, then download model weights from Hugging Face.
- Interfaces
- Shell-driven local inference and a Gradio browser interface.
- Platform requirements
- Python 3.10+, CUDA 12.4+, and a GPU with at least 40GB VRAM for inference.
- Supported workflows
- Text-to-image, text-to-video, image-to-video, image editing, video editing, image understanding, and video understanding.
- Training structure
- Fine-tuning code includes PyTorch Fully Sharded Data Parallel checkpoint support.
- Storage boundary
- The public training path supports local parquet files with embedded media bytes; remote TOS access is intentionally unavailable.
Read from README.md, requirements.txt, lance_gradio.py, inference_lance.py, data/tos.py, data/common.py, data/__init__.py, train/__init__.py, common/__init__.py, config/__init__.py, data/data_utils.py, data/transforms.py, train/fsdp_utils.py, modeling/__init__.py, data/dataset_base.py.
What it can do
Analyze and understand image content
Image file → Text description or analysis of image content
Analyze and understand video content
Video file → Text description or analysis of video content
Generate images from text prompts
Text description → Generated image
Generate videos from text prompts
Text description → Generated video
Edit existing images
Image file and editing instructions → Modified image
Edit existing videos
Video file and editing instructions → Modified video
Intel on Lance
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