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:
- dc08b27bddc05d9f4d45bf47643e89db27d43b4efb8a9e6423727b56a7c7dabf
- Size of remote file:
- 246 MB
- SHA256:
- 46e79b6ed5a1e1d603502a9a22a0b27704069373cdcfc937b17d63be0a50deac
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