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:
- e558f355c8936011abb356faa155f134fca332a31f405c26cc461baa6b9b6ba7
- Size of remote file:
- 1.69 MB
- SHA256:
- 196a0cebf25a49faff10e8da8123fd9759df1bc368aa3dbe48c48a133a331d04
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