Instructions to use abhiman23897/mnist_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abhiman23897/mnist_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("abhiman23897/mnist_diffusion", 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 abhiman23897/mnist_diffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.16 MB
-
https://huggingface.co/abhiman23897/mnist_diffusion/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://abhiman23897/mnist_diffusion/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/abhiman23897/mnist_diffusion/resolve/main/unet/diffusion_pytorch_model.bin
3.16 MB
- Xet hash:
- 62710437e85ccbebdf584416d5675201dfbfd98ea36403c8b2bf3b71840a8d04
- Size of remote file:
- 3.16 MB
- SHA256:
- ba501af45d2fd7a9c0f02d80fd942337f0e387235aafcbd3b8410e47e7b1efff
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