| --- |
| 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](https://huggingface.co/stabilityai/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](https://github.com/tangshunpu/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. |
|
|
| ```python |
| 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") |
| ``` |
|
|
