base_model: stabilityai/sd-turbo
pipeline_tag: image-to-image
tags:
- joint-source-channel-coding
- wireless-image-transmission
- semantic-communication
- one-step-diffusion
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")