Instructions to use stabilityai/stable-diffusion-xl-base-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stabilityai/stable-diffusion-xl-base-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
quantized 8 bit model?
#167
by Roronoa099 - opened
is there any 8 bit or 4 bit quantized model for this
In Brevitas (https://github.com/Xilinx/brevitas/tree/dev/src/brevitas_examples/stable_diffusion) we have a pipeline to quantize SDXL with several command line options to change the quantization configuration.
We support ONNX export to QCDQ format.
I am currently working on updating this script to improve and speed-up GPTQ computation, add FID score vs floating point version and other minor improvements.
You can find the PR here: https://github.com/Xilinx/brevitas/pull/951
Feel free to open an issue/discussion if you have any issue with this. It's being actively developed so bugs could be present.