Add OSD-JSCC and SwinJSCC (WITT C=16) weights

README.md

@@ -0,0 +1,39 @@
+---
+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: SNR-Adaptive One-Step Diffusion for
+Perceptual Wireless Image Transmission**. Code, usage instructions and a Colab
+demo are at **https://github.com/tangshunpu/OSD-JSCC**.
+
+| 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")
+```

osd_jscc_cr96_awgn.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b683b25d1cfba6ca0d6d2e5bae015343cae0757a06805fb92fce9af226d4530
+size 289915269

swinjscc_witt_c16_awgn.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50b0daa89c66090dbb7b2b17d64aeeb1ca491a17460d5ed622149959e5146d2f
+size 125745570