OSD-JSCC weights

Pretrained weights for OSD-JSCC: Neural Joint Source–Channel Coding with One-Step Diffusion. Code, usage instructions and a Colab demo are at https://github.com/tangshunpu/OSD-JSCC; the project page is https://tangshunpu.github.io/OSD_JSCC_Project/.

File	Model	Size
osd_jscc_cr96_awgn.pt	OSD-JSCC, AWGN channel, CR = 96, trained on 0–20 dB SNR	290 MB
swinjscc_witt_c16_awgn.pth	SwinJSCC baseline used in the paper's comparisons (WITT architecture, C = 16, CR = 96), AWGN, trained on DIV2K at 0–20 dB	126 MB

osd_jscc_cr96_awgn.pt holds only the weights trained for OSD-JSCC: the JSCC encoder and decoder, the latent adapter, the SNR embedding, the U-Net LoRA, conv_in/conv_out and the fine-tuned VAE decoder convolutions. It is loaded on top of SD-Turbo (U-Net, VAE and scheduler), which is downloaded separately and is subject to the Stability AI Community License.

swinjscc_witt_c16_awgn.pth contains the encoder and decoder of the WITT network in TF_SemCom at commit 75a581d. It was initialized from the official WITT C = 16 AWGN release and fine-tuned on DIV2K with SNR drawn from 0–20 dB.

from huggingface_hub import hf_hub_download

osd_ckpt = hf_hub_download("tangshunpu/OSD-JSCC", "osd_jscc_cr96_awgn.pt")
swin_ckpt = hf_hub_download("tangshunpu/OSD-JSCC", "swinjscc_witt_c16_awgn.pth")