Instructions to use samwell/sam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use samwell/sam with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("samwell/sam", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 33abf2f83325ca6cc42d3b77230edf67d976ada1e1577e812dd11f97fe7f0679
- Size of remote file:
- 246 MB
- SHA256:
- da950a01ac8abfb540d30cea64fd2acb1748456ad1dc1394731ebec876cee112
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