Instructions to use ByteDance/SDXL-Lightning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/SDXL-Lightning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/SDXL-Lightning", 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
- Local Apps
- Draw Things
- DiffusionBee
Update readme
Browse files
README.md
CHANGED
|
@@ -16,6 +16,7 @@ Our models are distilled from [stabilityai/stable-diffusion-xl-base-1.0](https:/
|
|
| 16 |
|
| 17 |
We provide both full UNet and LoRA checkpoints. The full UNet models have the best quality while the LoRA models can be applied to other base models.
|
| 18 |
|
|
|
|
| 19 |
|
| 20 |
## Diffusers Usage
|
| 21 |
|
|
|
|
| 16 |
|
| 17 |
We provide both full UNet and LoRA checkpoints. The full UNet models have the best quality while the LoRA models can be applied to other base models.
|
| 18 |
|
| 19 |
+
[Demo](https://huggingface.co/spaces/AP123/SDXL-Lightning) (made by the community)
|
| 20 |
|
| 21 |
## Diffusers Usage
|
| 22 |
|