Instructions to use scribbyotx/sa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use scribbyotx/sa with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("scribbyotx/sa", 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
File size: 617 Bytes
afebeda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | docker run -d -p 5000:5000 --gpus=all r8.im/xarty8932/dream@sha256:5e3c45aa9c9896f86634175309490225e5a379a6a81c39abbf55eab2cd16b657
curl -s -X POST \
-H "Content-Type: application/json" \
-d $'{
"input": {
"width": 1024,
"height": 1024,
"prompt": "An astronaut riding a rainbow unicorn",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.6,
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"high_noise_frac": 0.8,
"negative_prompt": "",
"prompt_strength": 0.8,
"num_inference_steps": 50
}
}' \
|