Instructions to use matgu23/rlst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use matgu23/rlst with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("matgu23/rlst", 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:
- 1b7e7719d68ac3d94e5b04d0612137dc75d867da4bf86f5f4b5a16f8e0ed0cb5
- Size of remote file:
- 246 MB
- SHA256:
- b05217d722789ebc96bae99950733d1e74b6760246d0bc6ba19437e0a1a0e272
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