Instructions to use dhifraan/delib2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dhifraan/delib2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dhifraan/delib2", 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 text_encoder/pytorch_model.bin from dhifraan/delib2: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/dhifraan/delib2/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://dhifraan/delib2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dhifraan/delib2/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 3ce6e66956ddf26c9c288553b65753b90016b3c7b5bd5224f6f12372fa003421
- Size of remote file:
- 1.36 GB
- SHA256:
- 96a7faba9e7b9fc42f54ac687934bb5b0f9ee8178c67441006697dcc20639199
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.