Instructions to use sankett/sd-sanket with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sankett/sd-sanket with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sankett/sd-sanket", 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 sankett/sd-sanket: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/sankett/sd-sanket/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://sankett/sd-sanket/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sankett/sd-sanket/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 8555582bfd8d5602697aa44b9404f30a22f44551b318090da613f294372e14c5
- Size of remote file:
- 492 MB
- SHA256:
- 5593f0cef6624ab1a51d0c8ca1be82969ca9898c108c2735011cd3b9a0406f90
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