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:
- 57fc996df9c1e1c0ea8558eced94b0a8091ca059520f933e19fb4d988c589be4
- Size of remote file:
- 3.44 GB
- SHA256:
- 5d85a9b48df1387628f9c1a4620dcc1af6799dfd0595a77e93fce8785dade3a7
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