Update README.md

README.md

@@ -13,6 +13,79 @@ We introduce **Generative Universal Verifier**, a novel concept and plugin desig
 
 OmniVerifier advances both reliable reflection during generation and scalable test-time refinement, marking a step toward more trustworthy and controllable next-generation reasoning systems.
 
+### Quick Start: Generated Image Verification
+
+Use the following code to test **OmniVerifier-7B** on a generated image:
+
+Please modify `image_path` and `prompt` to your own settings.
+
+The model will output both an **answer** and an **explanation**, indicating whether the image is strictly aligned with the given prompt.
+
+```python
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "comin/OmniVerifier-7B", torch_dtype=torch.bfloat16, device_map="auto"
+)
+# default processer
+processor = AutoProcessor.from_pretrained("comin/OmniVerifier-7B")
+
+image_path = '' # please replace it with your own image path
+prompt = '' # please replace it with the prompt you use to generate the image
+
+question = f"""This image was generated from the prompt: {prompt}. 
+    Please carefully analyze the image and determine whether all the objects, attributes, and spatial relationships mentioned in the prompt are correctly represented in the image. 
+
+    If the image accurately reflects the prompt, please answer 'true'; otherwise, answer 'false'.  
+
+    Respond strictly in the following JSON format: """ + """
+
+    {
+        "answer": true/false,
+        "explanation": "If the answer is false, briefly summarize the main error.",
+    }
+    """
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": image_path,
+            },
+            {"type": "text", "text": question},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
 
 ```
 @article{zhang2025generative,