Instructions to use acvlab/ABot-World-0-5B-LF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use acvlab/ABot-World-0-5B-LF 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("acvlab/ABot-World-0-5B-LF", 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
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* 🧠 **Open-Ended World Imagination:** Expands the world with new scenes and dynamics during rollout, avoiding scene lock-in, without prompt switching, by our *LongForcing* training.
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## 📢 News
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- 2026-08-03: We release the dataset of `500-hour` training data.
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- 2026-07-22: Release ABot-World-0 technical report.
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- 2026-07-13: ABot-World is now on [Reactor](https://reactor.inc/abot-world)!
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* 🧠 **Open-Ended World Imagination:** Expands the world with new scenes and dynamics during rollout, avoiding scene lock-in, without prompt switching, by our *LongForcing* training.
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## 📢 News
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- 2026-08-15: We released an experimental [24-hour uninterrupted rollout demo](https://amap-cvlab.github.io/abot-world-0-24h/). Check it out!
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- 2026-08-03: We release the dataset of `500-hour` training data.
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- 2026-07-22: Release ABot-World-0 technical report.
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- 2026-07-13: ABot-World is now on [Reactor](https://reactor.inc/abot-world)!
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