add 2x OTF GAN (default), 4 models total

README.md

@@ -18,7 +18,9 @@ of use*, not benchmark chasing: one file, one variant, and a checkpoint that
 converts cleanly to dynamic ONNX, TensorRT and ncnn — no fused/unfused pairs.
 
 - **2x / 4x release** → for **clean** images.
-- **4x OTF GAN** → for **degraded** images (blurry, noisy, JPEG-compressed).
+- **2x / 4x OTF GAN** → for **degraded** images (blurry, noisy, JPEG-compressed).
+
+Default is the **2x OTF GAN** (best all-round for degraded input); other models stay selectable.
 
 Models, ONNX/ncnn files, training configs and the architecture live at
 **[huggingface.co/Phips/NERVE](https://huggingface.co/Phips/NERVE)**.

app.py

@@ -19,6 +19,10 @@ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 REPO = "Phips/NERVE"
 MODELS: dict[str, tuple[str, int]] = {
+    "NERVE 2x — OTF GAN (blurry / noisy / JPEG input)": (
+        "models/2x_NERVE_OTF_gan.safetensors",
+        2,
+    ),
     "NERVE 2x — release (clean input)": ("models/2x_NERVE_release.safetensors", 2),
     "NERVE 4x — release (clean input)": ("models/4x_NERVE_release.safetensors", 4),
     "NERVE 4x — OTF GAN (blurry / noisy / JPEG input)": (
@@ -97,7 +101,7 @@ with gr.Blocks(title="NERVE", theme=gr.themes.Soft()) as demo:
             inp = gr.Image(type="pil", label="Input image", height=320)
             model = gr.Dropdown(
                 choices=list(MODELS),
-                value=list(MODELS)[1],
+                value=list(MODELS)[0],
                 label="Model",
             )
             btn = gr.Button("Upscale", variant="primary")