Improve model card: add pipeline tag, library name, and sample usage

This PR enhances the model card by:
- Adding `pipeline_tag: text-to-image` to improve discoverability on the Hugging Face Hub, allowing users to find the model via relevant filters.
- Adding `library_name: diffusers`, indicating compatibility and guiding users on which library to use for inference.
- Introducing a "Sample Usage" section to provide a clear path for users to get started with the model, referencing the official GitHub repository for detailed code and instructions.

README.md

@@ -1,8 +1,11 @@
 ---
-license: apache-2.0
 base_model:
 - stable-diffusion-v1-5/stable-diffusion-v1-5
+license: apache-2.0
+pipeline_tag: text-to-image
+library_name: diffusers
 ---
+
 <meta name="google-site-verification" content="-XQC-POJtlDPD3i2KSOxbFkSBde_Uq9obAIh_4mxTkM" />
 
 
@@ -60,6 +63,36 @@ The proposed framework is architected around two core components: SAA and IMR. (
 
 2. Download our SAA-related and IMR-related checkpoints from  [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID).
 
+## ⚡ Sample Usage (Diffusers)
+
+The official inference code is available in the [GitHub repository](https://github.com/ByteCat-bot/DynamicID), which provides detailed instructions for running the model. A typical usage with the `diffusers` library would involve loading the base Stable Diffusion pipeline and then integrating the DynamicID specific weights.
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+
+# Load the base Stable Diffusion pipeline
+# Ensure you have downloaded the base model locally or from Hugging Face Hub
+pipeline = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+).to("cuda")
+
+# Load DynamicID specific weights (e.g., LoRAs or custom UNet modifications)
+# The precise method for loading these weights will be detailed in the official repository.
+# For conceptual understanding, it might involve:
+# pipeline.load_lora_weights("path/to/DynamicID/weights")
+# Or integrating custom UNet/attention layers as per the DynamicID implementation.
+
+# Refer to the official GitHub repository for the exact loading and inference pipeline.
+# You would then pass your text prompt and identity reference images to the pipeline.
+# Example (conceptual):
+# prompt = "a photo of [person1] with a big smile and [person2] looking thoughtful"
+# generated_image = pipeline(
+#     prompt=prompt,
+#     identity_references=[id_image_1, id_image_2], # Placeholder for identity images
+#     # Add other parameters as specified in the DynamicID code
+# ).images[0]
+```
 
 ## 🌈 Gallery