gudaochangsheng/MaskUnet
[CVPR 2025] Official PyTorch implementation of Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
This project helps researchers and developers working with image generation models to improve the quality of their generated images. By selectively masking certain parts of the underlying U-Net model, it takes an existing diffusion model (like Stable Diffusion 1.5) and outputs higher-quality images. It's designed for machine learning engineers and researchers focused on computer vision and generative AI.
Use this if you are a machine learning researcher or engineer looking to enhance the output quality of your diffusion models without significantly increasing model complexity.
Not ideal if you are a general user looking for an out-of-the-box image generation tool without diving into model architecture or training/inference scripts.
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Python
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Last pushed
Mar 18, 2026
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