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
Standard Configuration
#36
by StarGazer-Media - opened
Can you provide the best configuration for this model:
Scheduler:
num_inference_step:
Guidance_scale:
Image Resolution:
Theoretically the model is trained for the following target settings:
- scheduler: Euler, with trailing timestep (sgm_uniform)
- num_inference_step: use the same inference step as the checkpoint.
- guidance_scale: 1
- resolution: 1024x1024