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
Quantization
Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference.
Learn how to quantize models in the Quantization guide.
PipelineQuantizationConfig
[[autodoc]] quantizers.PipelineQuantizationConfig
BitsAndBytesConfig
[[autodoc]] BitsAndBytesConfig
GGUFQuantizationConfig
[[autodoc]] GGUFQuantizationConfig
QuantoConfig
[[autodoc]] QuantoConfig
TorchAoConfig
[[autodoc]] TorchAoConfig
DiffusersQuantizer
[[autodoc]] quantizers.base.DiffusersQuantizer