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
Commit History
-
Upload sample_images with huggingface_hub
83db981 -
Upload model_index.json with huggingface_hub
77fe28b -
Upload vae with huggingface_hub
7567f39 -
Upload unet with huggingface_hub
519bc54 -
Upload tokenizer with huggingface_hub
848799e -
Upload text_encoder with huggingface_hub
cef3433 -
Upload scheduler with huggingface_hub
c5bd18b -
Upload safety_checker with huggingface_hub
ee5ddca -
Upload feature_extractor with huggingface_hub
2c77a4e -
Upload the concept Sam embeds and token
6504392 -
initial commit
2c8f121