Merge branch 'main' into dev

README.md

@@ -2,6 +2,8 @@
 
 Nunchaku is an inference engine designed for 4-bit diffusion models, as demonstrated in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). Please check [DeepCompressor](https://github.com/mit-han-lab/deepcompressor) for the quantization library.
 
+Chere [here](https://github.com/mit-han-lab/nunchaku/issues/149) to join our user groups on Slack and WeChat for discussions! If you have any questions, encounter issues, or are interested in contributing to the codebase, feel free to share your thoughts there!
+
 ### [Paper](http://arxiv.org/abs/2411.05007) | [Project](https://hanlab.mit.edu/projects/svdquant) | [Blog](https://hanlab.mit.edu/blog/svdquant) | [Demo](https://svdquant.mit.edu)
 
 - **[2025-02-20]** 🚀 We release the [pre-built wheels](https://huggingface.co/mit-han-lab/nunchaku) to simplify installation! Check [here](#Installation) for the guidance!
@@ -188,7 +190,7 @@ image = pipeline(
 image.save("flux.1-dev-ghibsky.png")
 ```
 
-**For ComfyUI users, we have implemented a node to convert the LoRA weights on the fly. All you need to do is specify the correct LoRA format. Please refer to Please refer to [comfyui/README.md](comfyui/README.md) for more details.**
+**For ComfyUI users, we have implemented a node to convert the LoRA weights on the fly. All you need to do is specify the correct LoRA format. Please refer to [comfyui/README.md](comfyui/README.md) for more details.**
 
 ## ComfyUI
 
@@ -249,6 +251,9 @@ If you find `nunchaku` useful or relevant to your research, please cite our pape
 * [QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://arxiv.org/abs/2405.04532), MLSys 2025
 * [SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers](https://arxiv.org/abs/2410.10629), ICLR 2025
 
+## Contact Us
+For enterprises interested in adopting SVDQuant or Nunchaku, including technical consulting, sponsorship opportunities, or partnership inquiries, please contact us at muyangli@mit.edu.
+
 ## Acknowledgments
 
 We thank MIT-IBM Watson AI Lab, MIT and Amazon Science Hub, MIT AI Hardware Program, National Science Foundation, Packard Foundation, Dell, LG, Hyundai, and Samsung for supporting this research. We thank NVIDIA for donating the DGX server.

src/Serialization.cpp

@@ -106,7 +106,9 @@ SafeTensors::SafeTensors(const std::string &filename) {
 }
 
 SafeTensors::~SafeTensors() {
+#ifndef _WIN32  
     checkCUDA(cudaHostUnregister(const_cast<char *>(this->mapped->data())));
+#endif
 }
 
 void SafeTensors::parseHeader() {