Text-to-Image
Diffusers
English
image-generation
subject-personalization
style-transfer
Diffusion-Transformer
Instructions to use bytedance-research/USO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/USO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bytedance-research/USO", 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

- Xet hash:
- 7c394b0afcfc480108450053bc261ddf0e7c3e0fa6c4423dddda76faf7abfc84
- Size of remote file:
- 1.67 MB
- SHA256:
- 543c724f6b929303046ae481672567fe4a9620f0af5ca1dfff215dc7a2cbff5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.