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
Download text_encoder/pytorch_model.bin from samwell/sam: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/samwell/sam/resolve/83db981dfd155c15f8e0d9cbf56da2fe87ec0f2e/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://samwell/sam@83db981dfd155c15f8e0d9cbf56da2fe87ec0f2e/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/samwell/sam/resolve/83db981dfd155c15f8e0d9cbf56da2fe87ec0f2e/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 33abf2f83325ca6cc42d3b77230edf67d976ada1e1577e812dd11f97fe7f0679
- Size of remote file:
- 246 MB
- SHA256:
- da950a01ac8abfb540d30cea64fd2acb1748456ad1dc1394731ebec876cee112
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