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:
- e767939b5ca8f003e181d92db7e2a1618dc48f35d49d78a67850a8f842255ddd
- Size of remote file:
- 6.59 MB
- SHA256:
- be0eb30b02ae7b33cbbb1fa24af76368ddc2d766d58a5a7423e9778bfc80d7d9
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