Instructions to use samwell/fastbreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use samwell/fastbreambooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("samwell/fastbreambooth", 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 samwell/fastbreambooth: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/samwell/fastbreambooth/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://samwell/fastbreambooth/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/samwell/fastbreambooth/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 96b4aa5befd42f70520ddf3d9d599d60bcf84ee20804025f5d80e655aeb7c8e9
- Size of remote file:
- 3.44 GB
- SHA256:
- ade3c5664e80ff99ebe004fb127723dbc8464e49b938debd8960da3a42254eb7
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