Instructions to use lsmpp/kontextrefiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lsmpp/kontextrefiner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lsmpp/kontextrefiner", 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
This pipeline is deprecated but it can still be used. However, we won't test the pipeline anymore and won't accept any changes to it. If you run into any issues, reinstall the last Diffusers version that supported this model.
K-Diffusion
k-diffusion is a popular library created by Katherine Crowson. We provide StableDiffusionKDiffusionPipeline and StableDiffusionXLKDiffusionPipeline that allow you to run Stable DIffusion with samplers from k-diffusion.
Note that most the samplers from k-diffusion are implemented in Diffusers and we recommend using existing schedulers. You can find a mapping between k-diffusion samplers and schedulers in Diffusers here
StableDiffusionKDiffusionPipeline
[[autodoc]] StableDiffusionKDiffusionPipeline
StableDiffusionXLKDiffusionPipeline
[[autodoc]] StableDiffusionXLKDiffusionPipeline