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
| 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 | |
| } | |
| }' \ | |