Improve model card: Add pipeline tag, update paper link, and add sample usage

This PR enhances the model card by:

- Adding the `pipeline_tag: text-to-image` metadata for improved model discoverability on the Hugging Face Hub.
- Updating the paper link from arXiv to the official Hugging Face Papers page: [IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design](https://huggingface.co/papers/2504.13176).
- Including a "Sample Usage" section with a Python code snippet, directly extracted from the GitHub README's "How to test" instructions, to guide users on how to run inference with the model.

README.md

@@ -1,20 +1,22 @@
 ---
-tags:
-  - text-to-image
-  - stable-diffusion
-  - garment generation
-  - multi-modality
-license: apache-2.0
 language:
-  - en
+- en
 library_name: diffusers
+license: apache-2.0
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- stable-diffusion
+- garment generation
+- multi-modality
 ---
+
 # IMAGGarment-1: Fine-Grained Garment Generation for Controllable Fashion Design
 
 
 <div align="center">
 
-[**Project Page**](https://revive234.github.io/imaggarment.github.io/) **|** [**Paper**](https://arxiv.org/pdf/2504.13176) **|** [**Code**](https://github.com/muzishen/IMAGGarment-1)
+[**Project Page**](https://revive234.github.io/imaggarment.github.io/) **|** [**Paper**](https://huggingface.co/papers/2504.13176) **|** [**Code**](https://github.com/muzishen/IMAGGarment-1)
 
 </div>
 
@@ -24,4 +26,22 @@ library_name: diffusers
 
 IMAGGarment-1 addresses the challenges of multi-conditional controllability in personalized fashion design and digital apparel applications. Specifically, IMAGGarment-1 employs a two-stage training strategy to separately model global appearance and local details, while enabling unified and controllable generation through end-to-end inference. In the first stage, we propose a global appearance model that jointly encodes silhouette and color using a mixed attention module and a color adapter. In the second stage, we present a local enhancement model with an adaptive appearance-aware module to inject user-defined logos and spatial constraints, enabling accurate placement and visual consistency.
 
-![framework](architecture.png)
+![framework](architecture.png)
+
+## Sample Usage
+
+To test the model, you can use the following inference code as demonstrated in the GitHub repository:
+
+```python
+python inference_IMAGGarment-1.py \
+--GAM_model_ckpt [GAM checkpoint] \
+--LEM_model_ckpt [LEM chekcpoint] \
+--sketch_path [your sketch path] \
+--logo_path [your logo path] \
+--mask_path [your mask path] \
+--color_path [your color path] \
+--prompt [your prompt] \
+--output_path [your save path] \
+--color_ckpt [color adapter checkpoint] \
+--device [your device]
+```