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
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README.md
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### 1-Step UNet
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```python
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
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### 1-Step UNet
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The 1-step model uses "sample" prediction instead of "epsilon" prediction! The scheduler needs to be configured correctly.
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```python
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
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