Add `pipeline_tag`, `library_name`, and GitHub badge to model card

This PR enhances the `Uni-MoE-2.0-Image` model card by adding key metadata and improving navigation:

* **`pipeline_tag: text-to-image`**: This makes the model discoverable under the text-to-image pipeline filter on the Hugging Face Hub, accurately reflecting its primary function as a visual generation model.
* **`library_name: transformers`**: This indicates compatibility with the Hugging Face Transformers library, enabling the automated "how to use" widget on the model page for convenient code snippets.
* **GitHub badge**: A direct link to the main GitHub repository (`https://github.com/HITsz-TMG/Uni-MoE`) has been added as a prominent badge at the top of the model card, improving accessibility for users looking for the project's code.

All existing descriptive content, including the paper link, project page link, and usage examples, remains unchanged to preserve the original author's information and adhere to best practices.

README.md

@@ -1,12 +1,14 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
+license: apache-2.0
 tags:
 - MoE
 - Unified Generation
 - Multi-modal
+pipeline_tag: text-to-image
+library_name: transformers
 ---
 
 <h1 align="center">Uni-MoE 2.0-Image</h1>
@@ -18,6 +20,7 @@ tags:
 <div align="center" style="display: flex; justify-content: center; margin-top: 10px;">
   <a href="https://idealistxy.github.io/Uni-MoE-v2.github.io/"><img src="https://img.shields.io/badge/📰 -Website-228B22" style="margin-right: 5px;"></a>
   <a href="https://arxiv.org/abs/2511.12609"><img src="https://img.shields.io/badge/📄-Paper-8A2BE2" style="margin-right: 5px;"></a>
+  <a href="https://github.com/HITsz-TMG/Uni-MoE"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github" style="margin-right: 5px;"></a>
 </div>
 
 ---
@@ -96,13 +99,15 @@ def load_unimoe(model_path: str):
 EXAMPLES = [
     # generation
     {
-        "prompt": "<image>\nImage generation: In the art piece, a realistically depicted young girl with flowing blonde hair gazes intently into the distance, her eyes reflecting the vibrant hues of a spring forest. The verdant greens and soft pastels of the budding trees are captured in subtle brushstrokes, giving the scene a serene and tranquil atmosphere. The minimalist composition focuses on the girl's expression of wonder and the lush woodland background, while the texture of the oil paint adds depth and richness to the canvas.",
+        "prompt": "<image>
+Image generation: In the art piece, a realistically depicted young girl with flowing blonde hair gazes intently into the distance, her eyes reflecting the vibrant hues of a spring forest. The verdant greens and soft pastels of the budding trees are captured in subtle brushstrokes, giving the scene a serene and tranquil atmosphere. The minimalist composition focuses on the girl's expression of wonder and the lush woodland background, while the texture of the oil paint adds depth and richness to the canvas.",
         "input_image": None,
         "out_name": "genarate.png",
     },
     # edition
     {
-        "prompt": "<image>\nAdd a dog standing near the fence in the foreground, close to the road.",
+        "prompt": "<image>
+Add a dog standing near the fence in the foreground, close to the road.",
         "input_image": "examples/assets/visual_gen/input_images/edit.jpg",
         "out_name": "edit.png",
     }
@@ -127,7 +132,8 @@ def run_batch(model_path: str, examples: List[Dict[str, Any]], save_dir: str):
     model, processor = load_unimoe(model_path)
 
     for i, ex in enumerate(examples, start=1):
-        print(f"\n=== [{i}/{len(examples)}]  prompt={ex['prompt']}")
+        print(f"
+=== [{i}/{len(examples)}]  prompt={ex['prompt']}")
         messages = make_message(ex['prompt'], ex.get('input_image'))
         print(messages)
 
@@ -180,7 +186,8 @@ def run_batch(model_path: str, examples: List[Dict[str, Any]], save_dir: str):
         )
 
         decoded = processor.batch_decode(output_ids[:, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)[0]
-        print("Generated text output:\n", decoded)
+        print("Generated text output:
+", decoded)
         print("Saved image to:", save_path)
 
 
@@ -197,4 +204,4 @@ Please cite the repo if you use the model or code in this repo.
 
 ```
 
-``` -->
+``` -->