Instructions to use frtna/ij-avatar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use frtna/ij-avatar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("frtna/ij-avatar", 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 frtna/ij-avatar: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/frtna/ij-avatar/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://frtna/ij-avatar/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/frtna/ij-avatar/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 2405ace3ca8e5951c2acc1e2d2a56779a232d922e81b8eff51ce1c076bfd7a3c
- Size of remote file:
- 681 MB
- SHA256:
- 3bfb1db0b80a5da44445cd298453620f6bd2bd1c1f803356d0ecc77b9a0f1bd9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.