Instructions to use jatmak/stein754 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jatmak/stein754 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("jatmak/stein754") prompt = "A cinematic wide shot of a vzx woman wearing iridescent futuristic armor, standing amidst the neon-lit rain of a cyberpunk Tokyo street." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 677fae47fbdb3afbb91a77a92b802054cbfa4b8984b983bf5197f8bf0b620622
- Size of remote file:
- 1.22 MB
- SHA256:
- 17d4bf5cd9afccae035cb01f988ced6417d3d9e09673e3f7d30abc35754c38cc
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