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
- Draw Things
- DiffusionBee
Improve model card: Correct library_name to diffusers and add full abstract
#5
by nielsr HF Staff - opened
This PR improves the model card for USO by:
- Correcting
library_name: Thelibrary_namehas been changed fromtransformerstodiffusers. This is based on the_diffusers_versionentry found in theconfig.jsonfile within the repository, and the model being a Diffusion Transformer (FLUX.1-dev based), which is indicative ofdiffuserscompatibility. This ensures that the automated code snippets generated by the Hugging Face Hub will accurately reflect the model's intended usage. - Adding the full paper abstract: The existing "Introduction" section has been replaced with the complete abstract provided in the paper details, and the section has been renamed to "Abstract". This provides a more comprehensive and standard overview of the model directly at the top of the model card.
All other sections, including the custom inference examples from the GitHub README, remain unchanged to preserve the original author's provided usage instructions.
fenfan changed pull request status to merged