Instructions to use ULZIITOGTOKH/road_obstacles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ULZIITOGTOKH/road_obstacles with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ULZIITOGTOKH/road_obstacles", 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:
- 339b8f207a3a11d6f02ef49e59f9d674b4dd4453cca083b284d715fc5ba107b3
- Size of remote file:
- 910 MB
- SHA256:
- f95926e3cba63700e38e453a177e50b109ab67d8dde4f0e125af162de4bcb950
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