Instructions to use ManuelRivoir/jormulator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ManuelRivoir/jormulator with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ManuelRivoir/jormulator", dtype=torch.bfloat16, device_map="cuda") prompt = "a beautiful painting of jrmnfnmyr" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from ManuelRivoir/jormulator: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/ManuelRivoir/jormulator/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://ManuelRivoir/jormulator/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ManuelRivoir/jormulator/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- d0cba219116f4170739e10dd865345bb13fb15a5d092b4e51469f418bcb27fac
- Size of remote file:
- 1.36 GB
- SHA256:
- bba9e8c3ef27bfdfd073e3af656b12950921412db12b74f3944099c181552c6d
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