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
Ctrl+K
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.
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