Instructions to use bytedance-research/OneReward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/OneReward with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bytedance-research/OneReward", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 80bd3163cebb1a212b2a3461fb00fd688d0817f3cacf49308d55035a13ad123e
- Size of remote file:
- 173 kB
- SHA256:
- 9f07e9d223939412d8baf5b38daeda15c54e37ef73c99d9932070ffba6d66c22
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