Instructions to use OmniGen2/OmniGen2-EditScore7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OmniGen2/OmniGen2-EditScore7B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OmniGen2/OmniGen2-EditScore7B", 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
- Xet hash:
- b21998c891f9eb6f24a4d8068e1567a628cdea991b416e7bbd893f88975fb77f
- Size of remote file:
- 81.8 MB
- SHA256:
- 647d7c38e07d3b17de44a2d1d3ebafb46ba34e76d199a468463d487bf9d6267b
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