keshik6/grafting
[NeurIPS 2025 Oral] Official Code for Exploring Diffusion Transformer Designs via Grafting
This project offers a method called 'grafting' to efficiently explore new designs for diffusion transformer models. It takes existing, pretrained diffusion transformers and allows you to modify their internal components, such as attention mechanisms or MLPs, without the extensive computational cost of training from scratch. This is for machine learning researchers and engineers who want to quickly experiment with and evaluate novel generative AI architectures.
Use this if you are a researcher or engineer looking to rapidly prototype and test new Diffusion Transformer architectures and evaluate their impact on image generation quality and speed.
Not ideal if you are looking for a plug-and-play solution for generating images without needing to delve into model architecture modifications.
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72
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2
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jan 09, 2026
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