| | --- |
| | license: mit |
| | --- |
| | # MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation |
| |
|
| | For detailed instructions on how to use the models and train them, please visit our [GitHub repository](https://github.com/showlab/MakeAnything). |
| |
|
| | ## Citation |
| | ``` |
| | @inproceedings{ |
| | Song2025MakeAnythingHD, |
| | title={MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation}, |
| | author={Yiren Song and Cheng Liu and Mike Zheng Shou}, |
| | year={2025}, |
| | url={https://api.semanticscholar.org/CorpusID:276107845} |
| | } |
| | ``` |
