Text-to-Image
Diffusers
Safetensors
DDMPipeline
diffusion
multi-expert
dit
laion
distributed
decentralized
flow-matching
Instructions to use bageldotcom/paris with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bageldotcom/paris with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bageldotcom/paris", 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
Bagel Labs commited on
arxiv citation
Browse files
README.md
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# Citation
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```bibtex
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@misc{
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title={Paris: A Decentralized Trained Open-Weight Diffusion Model},
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author={Jiang, Zhiying and Seraj, Raihan and Villagra, Marcos and Roy, Bidhan},
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year={2025},
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}
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```
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# Citation
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```bibtex
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@misc{jiang2025paris,
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title={Paris: A Decentralized Trained Open-Weight Diffusion Model},
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author={Jiang, Zhiying and Seraj, Raihan and Villagra, Marcos and Roy, Bidhan},
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year={2025},
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eprint={2510.03434},
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archivePrefix={arXiv},
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primaryClass={cs.GR},
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url={https://arxiv.org/abs/2510.03434}
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}
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```
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