Instructions to use MJ-Bench/DiffusionDPO-alignment-claude3-opus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MJ-Bench/DiffusionDPO-alignment-claude3-opus with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MJ-Bench/DiffusionDPO-alignment-claude3-opus", 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
- Draw Things
- DiffusionBee
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README.md
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image = pipeline(prompt=prompt, generator=generator, guidance_scale=gs).images[0]
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```
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image = pipeline(prompt=prompt, generator=generator, guidance_scale=gs).images[0]
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```
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## Citation
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```
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@misc{chen2024mjbenchmultimodalrewardmodel,
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title={MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?},
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author={Zhaorun Chen and Yichao Du and Zichen Wen and Yiyang Zhou and Chenhang Cui and Zhenzhen Weng and Haoqin Tu and Chaoqi Wang and Zhengwei Tong and Qinglan Huang and Canyu Chen and Qinghao Ye and Zhihong Zhu and Yuqing Zhang and Jiawei Zhou and Zhuokai Zhao and Rafael Rafailov and Chelsea Finn and Huaxiu Yao},
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year={2024},
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eprint={2407.04842},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2407.04842},
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}
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```
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