Instructions to use easylearning/FutureWorlds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use easylearning/FutureWorlds with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("easylearning/FutureWorlds", 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
Download code/pyproject.toml from easylearning/FutureWorlds: direct link, hf CLI and curl.
- Browser
- Download file 445 Bytes
-
https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/pyproject.toml
- Command line
-
hf download hf://easylearning/FutureWorlds/code/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/pyproject.toml
445 Bytes
| [build-system] | |
| requires = ["setuptools>=68"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "futureworlds" | |
| version = "0.1.0.dev0" | |
| description = "Memory-conditioned search and post-training for robotic world models" | |
| requires-python = ">=3.9" | |
| dependencies = ["numpy", "torch>=2.1", "transformers>=4.45,<5", "safetensors"] | |
| [project.optional-dependencies] | |
| hub = ["huggingface_hub>=0.34"] | |
| [tool.setuptools.packages.find] | |
| where = ["src"] | |