--- base_model: - Qwen/Qwen2.5-VL-3B-Instruct language: - en license: apache-2.0 tags: - gui - agent pipeline_tag: image-text-to-text library_name: transformers --- # InfiGUI-R1-3B This repository contains the model from the [InfiGUI-R1](https://arxiv.org/abs/2504.14239) 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: ```bash pip install transformers qwen-vl-utils ``` ### An Example of GUI Grounding & Trajectory Task ```python 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 tags." tool_prompt = " # Tools You may call one or more functions to assist with the user query. You are provided with function signatures within XML tags: {\"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\"}}} For each function call, return a json object with function name and arguments within XML tags: {\"name\": , \"arguments\": } " 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("")[-1] for ot in output_text] grounding_output = output_text[0].replace("```json", "").replace("```", "").strip() trajectory_output = output_text[1].replace("", "").replace("", "").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](https://github.com/Reallm-Labs/InfiGUI-R1).