Instructions to use ezlee258258/Inversion-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ezlee258258/Inversion-DPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ezlee258258/Inversion-DPO", 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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Official Inversion-DPO weights fine-tuned from Stable Diffusion XL. Only the trained UNet module is provided.
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**Paper**: [Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models](https://huggingface.co/papers/2507.11554)
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**Code Repository**: https://github.com/MIGHTYEZ/Inversion-DPO
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## Model Description
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Official Inversion-DPO weights fine-tuned from Stable Diffusion XL. Only the trained UNet module is provided.
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**Paper**: [Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models](https://huggingface.co/papers/2507.11554)
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**Code Repository**: https://github.com/MIGHTYEZ/Inversion-DPO
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## Model Description
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