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checkpoints/mim_final.pt

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checkpoints/psnr_0240000.pt

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checkpoints/psnr_final.pt

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checkpoints/translator_0020000.pt

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huggingface_model_card.md

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+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-to-image
+tags:
+  - image-restoration
+  - denoising
+  - image-denoising
+  - pytorch
+  - low-level-vision
+---
+
+# DenoiseGAN — detail-preserving single-step image denoiser
+
+A single-step image denoiser tuned to remove noise **while preserving fine
+detail**, plus a small **noise-translator** front-end that adapts it to noise it
+was not trained on. Code: **<your-github-url>**.
+
+## Files
+
+| File | What |
+|------|------|
+| `psnr_final.pt` | The denoiser (PSNR stage). Use the `ema` weights. |
+| `translator_0020000.pt` | Noise translator front-end (~0.37M params). Enable for Gaussian / OOD noise. |
+
+## Usage
+
+```python
+import torch
+from models import DenoiseGenerator, NoiseTranslator   # from the GitHub repo
+
+D = DenoiseGenerator(channels=(48,96,192,320,448), use_checkpoint=False).eval().cuda()
+D.load_state_dict(torch.load('psnr_final.pt')['ema'], strict=False)
+
+T = NoiseTranslator().eval().cuda()                      # optional
+T.load_state_dict(torch.load('translator_0020000.pt')['ema'])
+
+with torch.no_grad():
+    out = D(T(noisy))      # noisy normalized to [-1, 1]; drop T for in-distribution noise
+```
+
+## Model
+
+- **Generator** (~21M params): NAFNet/Restormer-hybrid U-Net, single forward pass,
+  residual noise prediction, attention-gated skips.
+- **Translator** (~0.37M params): bias-free, scale-equivariant residual CNN trained
+  *through* the frozen denoiser so `D(T(noisy)) ≈ clean`.
+
+## Notes & limitations
+
+- Shipped model is the **PSNR (reconstruction) stage**. The adversarial/GAN stage
+  was tried and **did not improve** results (it traded fidelity for hallucination).
+- Optimized for **detail preservation** on a specific noise family rather than for
+  topping a single PSNR benchmark.
+- For **Gaussian / out-of-distribution** noise, enable the **translator**.
+
+## License
+
+MIT.