Instructions to use chredl/Diffusion_Model_Vol_Surface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chredl/Diffusion_Model_Vol_Surface with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chredl/Diffusion_Model_Vol_Surface", 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:
- d693c9a6792e888a4d65595394603840aa928c05e018f3c642cc3a9926db30cb
- Size of remote file:
- 28.7 MB
- SHA256:
- 138276192f2651fa58cec25d9b6f50567332208adeefef99be2cdbe2fd70b57a
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