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
Link first author to homepage
Browse files
README.md
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> [**Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL**](https://arxiv.org/abs/2605.24001) (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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> [**Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL**](https://arxiv.org/abs/2605.24001) (NeurIPS 2026),
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> [Junyi Wu](https://junyi-w.github.io), Weijian Luo, Haoyang Zheng, Ruizhe Zhang, Guang Lin
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## Contact
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