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:
- 2d8209a8adc7719d8f688dc8fedf63536efaa8fbaad8f1a70cc65f0a4a1beb79
- Size of remote file:
- 882 kB
- SHA256:
- 7dbbefa4262e255ecc16b8bc260abf8f7745266e2f15cee2956e7fe0928b9e09
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