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
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README.md
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evospazio, SUV, front three-quarter view, full vehicle in frame,
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correct proportions, standard wheelbase,
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clean studio lighting, neutral background
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### 3️⃣ Reference image (training dataset)
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```markdown
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## Training Reference Image
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The following image is taken directly from the training dataset and shows
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evospazio, SUV, front three-quarter view, full vehicle in frame,
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correct proportions, standard wheelbase,
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clean studio lighting, neutral background
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```
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### 3️⃣ Reference image (training dataset)
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## Training Reference Image
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The following image is taken directly from the training dataset and shows
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