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/src/futureworlds/batch.py from easylearning/FutureWorlds: direct link, hf CLI and curl.
- Browser
- Download file 476 Bytes
-
https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/src/futureworlds/batch.py
- Command line
-
hf download hf://easylearning/FutureWorlds/code/src/futureworlds/batch.py
-
curl -L -o batch.py https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/src/futureworlds/batch.py
476 Bytes
| """Small tensor container for the upstream video reward's batch-only API.""" | |
| class TensorBatch: | |
| def __init__(self,batch,meta_info=None): | |
| self.batch=batch | |
| self.meta_info=meta_info or {} | |
| def from_single_dict(cls,batch,meta_info=None):return cls(batch,meta_info) | |
| def __len__(self):return len(next(iter(self.batch.values()))) | |
| def __getitem__(self,index):return type(self)({k:v[index] for k,v in self.batch.items()},self.meta_info) | |