Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
z-image
diffusion distillation
one-step generation
rlhf
Instructions to use Junyi-W/DIDR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Junyi-W/DIDR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Junyi-W/DIDR", 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
Point code links to github.com/Junyi-W/DIDR
Browse files
README.md
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> [**Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL**](https://github.com/Junyi-W/DIDR
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> Junyi Wu, Weijian Luo, Haoyang Zheng, Ruizhe Zhang, Guang Lin
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## Contact
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image=pipe(prompt=prompt, height=1024, width=1024, num_inference_steps=1, guidance_scale=0.0).images[0]
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```
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For more information, please refer to the [code repository](https://github.com/Junyi-W/DIDR
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## License
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> [**Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL**](https://github.com/Junyi-W/DIDR) (NeurIPS 2026),
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> Junyi Wu, Weijian Luo, Haoyang Zheng, Ruizhe Zhang, Guang Lin
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## Contact
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image=pipe(prompt=prompt, height=1024, width=1024, num_inference_steps=1, guidance_scale=0.0).images[0]
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```
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For more information, please refer to the [code repository](https://github.com/Junyi-W/DIDR)
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## License
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