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
Add library_name and pipeline_tag to metadata
#3
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
This PR improves the model card by adding:
library_name: diffusersandpipeline_tag: image-to-imageto the metadata for better discoverability and UI support.- The correct Arxiv paper ID (2508.21066) to replace the placeholder link in the header.
The code snippets provided by the authors demonstrate clear compatibility with the diffusers library.
XionghuiWang changed pull request status to merged