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
Question: mobile deployment β has anyone tested this?
#65
by 3morixd - opened
We test models on 40 phones (Snapdragon 865, 8GB RAM) at Dispatch AI (FZE, UAE).
Question: has anyone benchmarked this on mobile? Specifically:
- Inference speed (tokens/sec)?
- Model size after Q4_K_M quantization?
- RAM usage after load?
Happy to share our phone farm results if there's interest.
- Dispatch AI (FZE), Sharjah UAE