InfiGUI-R1-3B

This repository contains the model from the InfiGUI-R1 paper. The model is based on Qwen2.5-VL-3B-Instruct and trained using the proposed Actor2Reasoner framework, enhanced through reinforcement learning to improve its planning and reflection capabilities for GUI tasks.

Quick Start

Installation

First install required dependencies:

pip install transformers qwen-vl-utils

An Example of GUI Grounding & Trajectory Task

import cv2
import json
import torch
import requests
from PIL import Image
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info, smart_resize

MAX_IMAGE_PIXELS = 5600*28*28

# Load model and processor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Reallm-Labs/InfiGUI-R1-3B", 
    torch_dtype=torch.bfloat16, 
    attn_implementation="flash_attention_2", 
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("Reallm-Labs/InfiGUI-R1-3B", max_pixels=MAX_IMAGE_PIXELS, padding_side="left")

# Prepare image
img_url = "https://raw.githubusercontent.com/Reallm-Labs/InfiGUI-R1/main/images/test_img.png"
response = requests.get(img_url)
with open("test_img.png", "wb") as f:
    f.write(response.content)
image = Image.open("test_img.png")
width, height = image.size
new_height, new_width = smart_resize(height, width, max_pixels=MAX_IMAGE_PIXELS)

# Prepare inputs
instruction = "View detailed storage space usage"

system_prompt = "You FIRST think about the reasoning process as an internal monologue and then provide the final answer.
The reasoning process MUST BE enclosed within <think> </think> tags."
tool_prompt = "

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{\"type\": \"function\", \"function\": {\"name\": \"mobile_use\", \"description\": \"Use a touchscreen to interact with a mobile device, and take screenshots.\
* This is an interface to a mobile device with touchscreen. You can perform actions like clicking, typing, swiping, etc.\
* Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions.\
* The screen's resolution is " + str(new_width) + "x" + str(new_height) + ".\
* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don't click boxes on their edges unless asked.\", \"parameters\": {\"properties\": {\"action\": {\"description\": \"The action to perform. The available actions are:\
* `key`: Perform a key event on the mobile device.\
    - This supports adb's `keyevent` syntax.\
    - Examples: \\\"volume_up\\\", \\\"volume_down\\\", \\\"power\\\", \\\"camera\\\", \\\"clear\\\".\
* `click`: Click the point on the screen with coordinate (x, y).\
* `long_press`: Press the point on the screen with coordinate (x, y) for specified seconds.\
* `swipe`: Swipe from the starting point with coordinate (x, y) to the end point with coordinates2 (x2, y2).\
* `type`: Input the specified text into the activated input box.\
* `system_button`: Press the system button.\
* `open`: Open an app on the device.\
* `wait`: Wait specified seconds for the change to happen.\
* `terminate`: Terminate the current task and report its completion status.\", \"enum\": [\"key\", \"click\", \"long_press\", \"swipe\", \"type\", \"system_button\", \"open\", \"wait\", \"terminate\"], \"type\": \"string\"}, \"coordinate\": {\"description\": \"(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by `action=click`, `action=long_press`, and `action=swipe`.\", \"type\": \"array\"}, \"coordinate2\": {\"description\": \"(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to. Required only by `action=swipe`.\", \"type\": \"array\"}, \"text\": {\"description\": \"Required only by `action=key`, `action=type`, and `action=open`.\", \"type\": \"string\"}, \"time\": {\"description\": \"The seconds to wait. Required only by `action=long_press` and `action=wait`.\", \"type\": \"number\"}, \"button\": {\"description\": \"Back means returning to the previous interface, Home means returning to the desktop, Menu means opening the application background menu, and Enter means pressing the enter. Required only by `action=system_button`\", \"enum\": [\"Back\", \"Home\", \"Menu\", \"Enter\"], \"type\": \"string\"}, \"status\": {\"description\": \"The status of the task. Required only by `action=terminate`.\", \"type\": \"string\", \"enum\": [\"success\", \"failure\"]}}, \"required\": [\"action\"], \"type\": \"object\"}}}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{\"name\": <function-name>, \"arguments\": <args-json-object>}
</tool_call>"
grounding_prompt = f'''The screen's resolution is {new_width}x{new_height}.
Point to the UI element most relevant to "{instruction}", output its coordinates using JSON format:
```json
[
    {{"point_2d": [x, y], "label": "object name/description"}}
]```'''
trajectory_prompt = f"The user query:  {instruction}
Task progress (You have done the following operation on the current device): "


# Build messages
grounding_messages = [
    {"role": "system", "content": system_prompt},
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "test_img.png"},
            {"type": "text", "text": grounding_prompt}
        ]
    }
]
trajectory_messages = [
    {"role": "system", "content": system_prompt + tool_prompt},
    {
        "role": "user",
        "content": [
            {"type": "text", "text": trajectory_prompt},
            {"type": "image", "image": "test_img.png"}
        ],
    },
]
messages = [grounding_messages, trajectory_messages]

# Process and generate
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").to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=512)
output_text = processor.batch_decode(
    [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

# Visualize results
output_text = [ot.split("</think>")[-1] for ot in output_text]

grounding_output = output_text[0].replace("```json", "").replace("```", "").strip()
trajectory_output = output_text[1].replace("<tool_call>", "").replace("</tool_call>", "").strip()

try:
    grounding_output = json.loads(grounding_output)
    trajectory_output = json.loads(trajectory_output)

    grounding_coords = grounding_output[0]['point_2d']
    trajectory_coords = trajectory_output["arguments"]['coordinate'] if "coordinate" in trajectory_output["arguments"] else None

    grounding_label = grounding_output[0]['label']
    trajectory_label = json.dumps(trajectory_output["arguments"])

    # Load the original image
    img = cv2.imread("test_img.png")
    if img is None:
        raise ValueError("Could not load the image")
    
    height, width = img.shape[:2]
    
    # Create copies for each visualization
    grounding_img = img.copy()
    trajectory_img = img.copy()
    
    # Visualize grounding coordinates
    if grounding_coords:
        x = int(grounding_coords[0] / new_width * width)
        y = int(grounding_coords[1] / new_height * height)
        
        cv2.circle(grounding_img, (x, y), 10, (0, 0, 255), -1)
        cv2.putText(grounding_img, grounding_label, (x+10, y-10),
                    cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
        cv2.imwrite("grounding_output.png", grounding_img)
        print("Predicted coordinates:", grounding_coords)
        print(f"Grounding visualization saved to grounding_output.png")
    
    # Visualize trajectory coordinates
    if trajectory_coords:
        x = int(trajectory_coords[0] / new_width * width)
        y = int(trajectory_coords[1] / new_height * height)
        
        cv2.circle(trajectory_img, (x, y), 10, (0, 0, 255), -1)
        cv2.putText(trajectory_img, trajectory_label, (x+10, y-10),
                    cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
        cv2.imwrite("trajectory_output.png", trajectory_img)
        print("Predicted action:", trajectory_label)
        print(f"Trajectory visualization saved to trajectory_output.png")

except:
    print("Error: Failed to parse coordinates or process image")

For more information, please refer to our repo.