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 vase/text_encoder/pytorch_model.bin from ImagenHub/DreamBooth-Models: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/ImagenHub/DreamBooth-Models/resolve/main/vase/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://ImagenHub/DreamBooth-Models/vase/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ImagenHub/DreamBooth-Models/resolve/main/vase/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- b70e3ec2a7be0d2b6c5a57af0a28fbf7b49ea18cc24a90350276440132a92706
- Size of remote file:
- 492 MB
- SHA256:
- c7cdbfc7d10e7322338f0883137fa94ed05b66829a7a7e298f27e16aec6c47da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.