Instructions to use jafetsierra/rem_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jafetsierra/rem_output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jafetsierra/rem_output") prompt = "a picture of rem a girl character from re zero. she wears a maid costume, has blue eyes and a big breast" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- f0ea477e6431b912f7b5a68a1a580b760475ee9e5c119dca9f4ee5a03ddfdcc5
- Size of remote file:
- 6.59 MB
- SHA256:
- a2530e862a5bca335f707ca7e81b385511a587b7a178b24eaf3142357d3816d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.