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
Inpainting
The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.
Tips
It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such as runwayml/stable-diffusion-inpainting. Default text-to-image Stable Diffusion checkpoints, such as stable-diffusion-v1-5/stable-diffusion-v1-5 are also compatible but they might be less performant.
Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations!
StableDiffusionInpaintPipeline
[[autodoc]] StableDiffusionInpaintPipeline - all - call - enable_attention_slicing - disable_attention_slicing - enable_xformers_memory_efficient_attention - disable_xformers_memory_efficient_attention - load_textual_inversion - load_lora_weights - save_lora_weights
StableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
FlaxStableDiffusionInpaintPipeline
[[autodoc]] FlaxStableDiffusionInpaintPipeline - all - call
FlaxStableDiffusionPipelineOutput
[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput