Instructions to use rihamsharp/123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rihamsharp/123 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rihamsharp/123", 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
- Xet hash:
- 1a229922ca35a7f95d872812171e212e52a87132156813c81f5c6147cd091613
- Size of remote file:
- 2.13 GB
- SHA256:
- 728903645b846e5556901edcd53084bfcc28fbc5baf05cd81909a3310aa69157
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