Beinsezii/skrample
Composable sampling functions for diffusion models
This project offers a highly flexible toolkit for researchers and engineers working with diffusion models to generate high-quality images or other media. It takes existing diffusion models and allows for precise control over the image generation process, letting users experiment with different sampling methods, schedules, and noise types. The output is more refined and controlled generated content.
Use this if you are a machine learning engineer or researcher developing advanced generative AI models and need fine-grained control over the sampling process to optimize output quality or explore novel generation techniques.
Not ideal if you are an end-user simply looking to generate images without needing to delve into the technical specifics of model sampling.
Stars
8
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 18, 2026
Commits (30d)
0
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