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
Models
🤗 Diffusers provides pretrained models for popular algorithms and modules to create custom diffusion systems. The primary function of models is to denoise an input sample as modeled by the distribution .
All models are built from the base [ModelMixin] class which is a torch.nn.Module providing basic functionality for saving and loading models, locally and from the Hugging Face Hub.
ModelMixin
[[autodoc]] ModelMixin
FlaxModelMixin
[[autodoc]] FlaxModelMixin
PushToHubMixin
[[autodoc]] utils.PushToHubMixin