Instructions to use robbyant/lingbot-world-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use robbyant/lingbot-world-fast with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("robbyant/lingbot-world-fast", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Add WBench evaluation results
Browse filesThis PR adds this model's results on [**WBench**](https://github.com/meituan-longcat/WBench) — a multi-turn benchmark for interactive video world models (289 cases, 22 metrics, 5 dimensions), now registered as a benchmark on the Hub.
Scores link to the [leaderboard](https://meituan-longcat.github.io/WBench/). Feel free to close if you'd prefer not to include it — thanks for the great work!
.eval_results/wbench.yaml
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- dataset:
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id: meituan-longcat/WBench
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task_id: wbench_navi
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value: 78.8
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source:
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url: https://meituan-longcat.github.io/WBench/
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name: WBench Leaderboard
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