Instructions to use nikhilkr/sdnikrt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nikhilkr/sdnikrt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nikhilkr/sdnikrt", 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 unet/diffusion_pytorch_model.bin from nikhilkr/sdnikrt: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/nikhilkr/sdnikrt/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://nikhilkr/sdnikrt/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/nikhilkr/sdnikrt/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 8cd1fd97ba26a5a1a48a4863a211636a305c0c64b7849e6a0e514be816aa1c5d
- Size of remote file:
- 3.44 GB
- SHA256:
- d0d3d34f92a22889c83b1a41ee7ea8c2e5b5e919c4c365bdae8b2090445fe953
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