Instructions to use ImagenHub/DreamBooth-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ImagenHub/DreamBooth-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ImagenHub/DreamBooth-Models", 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 can/vae/diffusion_pytorch_model.bin from ImagenHub/DreamBooth-Models: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/ImagenHub/DreamBooth-Models/resolve/main/can/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://ImagenHub/DreamBooth-Models/can/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/ImagenHub/DreamBooth-Models/resolve/main/can/vae/diffusion_pytorch_model.bin
335 MB
- Xet hash:
- e2e70a6696fff1ef706cc1ad68ac9f21c1cdbb5b2d36311725a6f49a0fecce99
- Size of remote file:
- 335 MB
- SHA256:
- c02e0c7c263a4c0630ca3a72380ff55b9e38e0ab41d64dff7bf620a58342bc75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.