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-OneRewardDynamic-transformer /diffusion_pytorch_model-00003-of-00003.safetensors
- Xet hash:
- d73e6c566ae010d884457d77ac31be72526ce71bd43024b02e1b5db711dc443e
- Size of remote file:
- 3.87 GB
- SHA256:
- 56bd2363e89d57434fef5ed3b77c5eb7c73bfbb82613ddb210271ba16cb4b36e
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