Instructions to use ooutlierr/fuse-dit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ooutlierr/fuse-dit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ooutlierr/fuse-dit", 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
Update README.md to include some samples
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by sayakpaul HF Staff - opened
README.md
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# Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
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## Resources
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- [arXiv: Paper](https://arxiv.org/pdf/2505.10046)
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- [GitHub: Code](https://github.com/tang-bd/fuse-dit)
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# Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
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<div align="center">
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<img src="https://github.com/tang-bd/fuse-dit/blob/main/assets/visual.jpg?raw=true" width="95%"/>
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</div>
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## Resources
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- [arXiv: Paper](https://arxiv.org/pdf/2505.10046)
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- [GitHub: Code](https://github.com/tang-bd/fuse-dit)
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