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
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by nielsr HF Staff - opened
README.md
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license: cc-by-nc-4.0
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base_model:
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- black-forest-labs/FLUX.1-Fill-dev
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language:
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- en
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# OneReward
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Official checkpoint of **[OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning](https://arxiv.org/abs/
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[](https://arxiv.org/abs/2508.21066) [](https://github.com/bytedance/OneReward) [](https://one-reward.github.io/)
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<br>
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<p align="center">
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