Xiuyu-Li/q-diffusion
[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.
This project helps machine learning engineers and researchers optimize their diffusion models for image generation. It takes a full-precision diffusion model and converts it into a lower-precision, more efficient version without significant loss in image quality. The output is a "quantized" model that generates high-quality images much faster, ideal for deploying image generation systems more efficiently.
371 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher working with diffusion models for image synthesis and need to significantly speed up image generation or reduce computational requirements without sacrificing quality.
Not ideal if you are not familiar with machine learning model quantization or deep learning frameworks, as it requires a technical understanding of diffusion model implementation.
Stars
371
Forks
26
Language
Python
License
MIT
Category
Last pushed
Mar 21, 2024
Commits (30d)
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