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
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| # AutoPipeline | |
| The `AutoPipeline` is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the `AutoPipeline` automatically retrieves the correct pipeline class from the checkpoint `model_index.json` file. | |
| > [!TIP] | |
| > Check out the [AutoPipeline](../../tutorials/autopipeline) tutorial to learn how to use this API! | |
| ## AutoPipelineForText2Image | |
| [[autodoc]] AutoPipelineForText2Image | |
| - all | |
| - from_pretrained | |
| - from_pipe | |
| ## AutoPipelineForImage2Image | |
| [[autodoc]] AutoPipelineForImage2Image | |
| - all | |
| - from_pretrained | |
| - from_pipe | |
| ## AutoPipelineForInpainting | |
| [[autodoc]] AutoPipelineForInpainting | |
| - all | |
| - from_pretrained | |
| - from_pipe | |