Instructions to use Gimbor/cuth123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gimbor/cuth123 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("Gimbor/cuth123") prompt = "A futuristic neon-drenched cyberpunk cityscape where a majestic golden lion with holographic wings stalks through the rain, Cuth123, cinematic lighting." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- bff6820b08ba72b8ccac73a28bf2547428ae6ea1966f292e84266a324079eb62
- Size of remote file:
- 195 MB
- SHA256:
- 11d903e0d91937dc6d1de559a225e43860f7c4fabba5b9c3bddd3b109eca77c5
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