Instructions to use Samit/samit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Samit/samit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Samit/samit", 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 unet with huggingface_hub
e979dd3 -
Upload tokenizer with huggingface_hub
ad734ee -
Upload text_encoder with huggingface_hub
183b257 -
Upload scheduler with huggingface_hub
5867c09 -
Upload safety_checker with huggingface_hub
87cfc49 -
Upload feature_extractor with huggingface_hub
8538019 -
Upload the concept samit embeds and token
0181cb9 -
initial commit
f9b59cb