Instructions to use CodeGoat24/FLUX.2-klein-base-9B-UnifiedReward-Flex-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CodeGoat24/FLUX.2-klein-base-9B-UnifiedReward-Flex-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CodeGoat24/FLUX.2-klein-base-9B-UnifiedReward-Flex-lora", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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@@ -53,7 +53,7 @@ For further details, please refer to the following resources:
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```bibtex
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@article{unifiedreward-flex,
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title={Unified Personalized Reward Model for Vision Generation},
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author={Wang, Yibin and Zang, Yuhang and Han, Feng and
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journal={arXiv preprint arXiv:2602.02380},
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year={2026}
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}
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```bibtex
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@article{unifiedreward-flex,
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title={Unified Personalized Reward Model for Vision Generation},
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author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
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journal={arXiv preprint arXiv:2602.02380},
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year={2026}
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}
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