Instructions to use p1atdev/plat-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use p1atdev/plat-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("p1atdev/plat-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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download unet/diffusion_pytorch_model.bin from p1atdev/plat-diffusion: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/p1atdev/plat-diffusion/resolve/refs%2Fpr%2F3/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://p1atdev/plat-diffusion@refs/pr/3/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/p1atdev/plat-diffusion/resolve/refs%2Fpr%2F3/unet/diffusion_pytorch_model.bin
3.46 GB
- Xet hash:
- 04094c2517931cf34faf8b7e357d9ac5a3cf3815388f5b27d9a8e7906f619c42
- Size of remote file:
- 3.46 GB
- SHA256:
- 1ee56a201b46af5e9178be6f4de76c546b0d4cf56e77267714175bde7f25d591
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