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
Rename README.md to A realistic scientific illustration of wastewater purification using Hydrogel-COF. On the left, show dark, polluted water containing visible plastic bottles and plastic bags. In the middle, highlight a Hydrogel-COF purification membrane layer. On the right, show clean, crystal-clear natural water flowing out, resembling safe drinking water. The scene should look realistic, with clear contrast between dirty polluted water and fresh clean water.
#7
by magdaaboelanwar - opened
A realistic scientific illustration of wastewater purification using Hydrogel-COF. On the left, show dark, polluted water containing visible plastic bottles and plastic bags. In the middle, highlight a Hydrogel-COF purification membrane layer. On the right, show clean, crystal-clear natural water flowing out, resembling safe drinking water. The scene should look realistic, with clear contrast between dirty polluted water and fresh clean water.
fenfan changed pull request status to closed