Instructions to use rorito/jugxl9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rorito/jugxl9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rorito/jugxl9") prompt = "a young woman, swimming cap, brown hair, green eyes, upper body, from above, facing the viewer, floating on the water, wet, pensive, one-piece swimming suit, realistic, skin texture, best quality" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
jugger

- Prompt
- a young woman, swimming cap, brown hair, green eyes, upper body, from above, facing the viewer, floating on the water, wet, pensive, one-piece swimming suit, realistic, skin texture, best quality
- Negative Prompt
- bad quality, worst quality, low quality, semi realistic, 2d, cartoon,
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for rorito/jugxl9
Base model
stabilityai/stable-diffusion-xl-base-1.0