Instructions to use SidXXD/f-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/f-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/f-2", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <v1*> person" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ef9dd9f03a27225709bf48501571a8a10c2fa59ad2b8a393bd08fc4f3b4ad223
- Size of remote file:
- 76.7 MB
- SHA256:
- 9b97161f4b36a99f48073867a7ab10cf5917d01f8409b39e1f7a1ec3edc1bdc6
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