Instructions to use mynameisai/jq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mynameisai/jq with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mynameisai/jq", 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 mynameisai/jq: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/mynameisai/jq/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://mynameisai/jq/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mynameisai/jq/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 002777e366c2995b48305321743e43e8aa38d160c46dd8c46a30a921f671ae91
- Size of remote file:
- 492 MB
- SHA256:
- d72f8ec8919cf95bd92fab7531f89214d96a58a5efda61ddea12a1258fa9234a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.