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README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - diffusion
+  - retinal-fundus
+  - diabetic-retinopathy
+  - medical-imaging
+  - image-to-image
+  - conditional-generation
+  - stable-diffusion
+datasets:
+  - usama10/retinal-dr-longitudinal
+pipeline_tag: image-to-image
+---
+
+# Conditional Latent Diffusion Model for Retinal Future-State Synthesis
+
+Trained model weights for predicting two-year follow-up retinal fundus images from baseline photographs and clinical metadata.
+
+## Model Description
+
+This model adapts Stable Diffusion 1.5 for longitudinal retinal image prediction. It consists of two components:
+
+1. **Fine-tuned VAE** (`vae_best.pt`, 320 MB): SD 1.5 VAE encoder/decoder fine-tuned on retinal fundus images with L1 + SSIM + LPIPS + KL loss. Achieves SSIM 0.954 on reconstruction.
+
+2. **Conditional U-Net** (`diffusion_best.pt`, 13 GB): 860M-parameter denoising U-Net with 15-channel input (4 noisy latent + 4 baseline latent + 7 clinical feature maps). Trained for 500 epochs with cosine LR schedule, EMA, and classifier-free guidance dropout.
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| SSIM | 0.762 |
+| PSNR | 17.26 dB |
+| LPIPS | 0.379 |
+| FID | 107.28 |
+
+Evaluated on 110 held-out test image pairs.
+
+## Usage
+
+```python
+import torch
+from diffusers import AutoencoderKL, UNet2DConditionModel
+
+# Load VAE
+vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
+vae_state = torch.load("vae_best.pt", map_location="cpu")
+if "model_state_dict" in vae_state:
+    vae_state = vae_state["model_state_dict"]
+vae.load_state_dict(vae_state, strict=False)
+
+# Load U-Net (requires modified conv_in for 15 input channels)
+unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+# ... modify conv_in and load checkpoint
+# See full inference code at the GitHub repository
+```
+
+## Links
+
+- **Code:** [github.com/Usama1002/retinal-diffusion](https://github.com/Usama1002/retinal-diffusion)
+- **Dataset:** [huggingface.co/datasets/usama10/retinal-dr-longitudinal](https://huggingface.co/datasets/usama10/retinal-dr-longitudinal)
+
+## Citation
+
+```bibtex
+@article{usama2026retinal,
+  title={Conditional Latent Diffusion for Predictive Retinal Fundus Image Synthesis from Baseline Imaging and Clinical Metadata},
+  author={Usama, Muhammad and Pazo, Emmanuel Eric and Li, Xiaorong and Liu, Juping},
+  journal={Computers in Biology and Medicine (under review)},
+  year={2026}
+}
+```
+
+## License
+
+CC BY-NC 4.0. Non-commercial research use only.

diffusion_best.pt

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vae_best.pt

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