Instructions to use samuelchristlie/Lance-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use samuelchristlie/Lance-GGUF with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("samuelchristlie/Lance-GGUF", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Lance-GGUF
Direct GGUF Conversion of bytedance-research/Lance
Lance is a lightweight native unified multimodal model that supports image and video understanding, generation, and editing within a single framework. With only 3B active parameters, it delivers strong performance across image generation, image editing, video generation, and video understanding benchmarks β trained entirely from scratch.
Table of Contents π
- βΆ Usage
- π License
- π Acknowledgements
βΆ Usage
Download models using huggingface-cli:
pip install "huggingface_hub[cli]"
huggingface-cli download samuelchristlie/Lance-GGUF --local-dir ./Lance-GGUF
You can also download directly from this page.
π License
This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license.
π Acknowledgements
Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/
ByteDance Research for the Lance model https://huggingface.co/bytedance-research/Lance
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