readme_normalize

README.md

@@ -10,4 +10,24 @@ pinned: false
 short_description: 'RAM++: Robust Representation Learning via Adaptive Mask'
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# RAM++
+
+Online demo for **RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration**.  
+At inference we use **RestormerRFR** (restoration backbone) + **DINOv2** semantic features for robust, content-aware restoration across noise, blur, compression artifacts, low-light, and mixed degradations.
+
+## What you can do
+- Upload a **JPEG/PNG** → click **Run (ZeroGPU)** → get the restored image.  
+- Use the provided **Examples** to try quickly.  
+- Call the **API** for batch processing.
+
+## Citation
+```bibtex
+@misc{zhang2025ramrobustrepresentationlearning,
+  title        = {RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration},
+  author       = {Zilong Zhang and Chujie Qin and Chunle Guo and Yong Zhang and Chao Xue and Ming-Ming Cheng and Chongyi Li},
+  year         = {2025},
+  eprint       = {2509.12039},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.CV},
+  url          = {https://arxiv.org/abs/2509.12039}
+}

app.py

@@ -83,11 +83,8 @@ def get_model_and_device():
     return model, device
 
 
-@spaces.GPU(duration=60)
+@spaces.GPU(duration=120)
 def restore_image(pil_img: Image.Image) -> Image.Image:
-    """
-    输入一张图片，输出复原后的图片（与 RAM++ RestormerRFR + DINO 特征推理一致）
-    """
     try:
         model, device = get_model_and_device()
         dino_extractor = get_dino_extractor(device)
@@ -109,7 +106,6 @@ def restore_image(pil_img: Image.Image) -> Image.Image:
 
         output = normalize(output, -1 * mean / std, 1 / std)
         output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()  # (3,H,W)
-        output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))  # (H,W,RGB)
         output = (output * 255.0).round().astype(np.uint8)
         out_pil = Image.fromarray(output, mode="RGB")
         return out_pil
@@ -117,7 +113,7 @@ def restore_image(pil_img: Image.Image) -> Image.Image:
         raise gr.Error(f"{e}\n{traceback.format_exc()}")
 
 DESCRIPTION = """
-# RAM++: Robust Representation Learning via  Adaptive Mask for All-in-One Image Restoration
+# RAM++
 """
 
 with gr.Blocks(title="RAM++ ZeroGPU Demo") as demo: