Instructions to use francipam/evospazio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use francipam/evospazio with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("francipam/evospazio", 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:
- 7aac306d8cc8101baa9df0bb247583d00a7a08582065c0f1f88e34393519f3dc
- Size of remote file:
- 830 kB
- SHA256:
- 213519a830e068179a94d2a5f0f9e33680940a29bbeaccc5ef5dae0182d7ec47
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