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 Settings
- Draw Things
- DiffusionBee
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README.md
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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.
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## Diffusers Usage
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Please always use the correct checkpoint for the corresponding inference steps.
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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.
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## Demos
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The community has made some amazing demos based on SDXL-Lightning.
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Here we feature some of the best:
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* [Generate with all configurations](https://huggingface.co/spaces/AP123/SDXL-Lightning)
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* [Real-time generation as you type](https://fastsdxl.ai/)
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* [Comparison to other models](https://anotherjesse.com/posts/sdxl-lightning/)
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## Diffusers Usage
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Please always use the correct checkpoint for the corresponding inference steps.
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