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
OneReward / flux.1-fill-dev-OneReward-transformer /diffusion_pytorch_model-00002-of-00003.safetensors
- Xet hash:
- 9972d4c8bcb2b2140575804cf34b4272e3a6bfca338eac630ac49423f71734c2
- Size of remote file:
- 9.95 GB
- SHA256:
- 9dc638253d98df623c246c8b23123515d5b5a2d71d912d256177b3fee9e5f688
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.