Instructions to use shalpin87/diffusion_conditional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shalpin87/diffusion_conditional with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shalpin87/diffusion_conditional", 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
Download unet/diffusion_pytorch_model.bin from shalpin87/diffusion_conditional: direct link, hf CLI and curl.
- Browser
- Download file 182 MB
-
https://huggingface.co/shalpin87/diffusion_conditional/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://shalpin87/diffusion_conditional/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/shalpin87/diffusion_conditional/resolve/main/unet/diffusion_pytorch_model.bin
182 MB
- Xet hash:
- 0cb6a128ee0c738dea8504ec64376c2b9edd07e881308e7da41e2e5c01813d16
- Size of remote file:
- 182 MB
- SHA256:
- 43ec25f1c68678c6a21e0412e38fe1466a76ce336e58414e01b4fd8a3c9c0376
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