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 paper name
#2
by sayakpaul HF Staff - opened
README.md
CHANGED
|
@@ -2,6 +2,10 @@
|
|
| 2 |
license: apache-2.0
|
| 3 |
---
|
| 4 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
## Resources
|
| 6 |
- [arXiv: Paper](https://arxiv.org/pdf/2505.10046)
|
| 7 |
- [GitHub: Code](https://github.com/tang-bd/fuse-dit)
|
|
|
|
| 2 |
license: apache-2.0
|
| 3 |
---
|
| 4 |
|
| 5 |
+
# Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
|
| 6 |
+
|
| 7 |
+
We systematically explore the under-designed space of fusing LLMs with DiT-based diffusion backbones to text-to-image synthesis.
|
| 8 |
+
|
| 9 |
## Resources
|
| 10 |
- [arXiv: Paper](https://arxiv.org/pdf/2505.10046)
|
| 11 |
- [GitHub: Code](https://github.com/tang-bd/fuse-dit)
|