├── .github └── workflows │ └── test.yml ├── LICENSE ├── README.md ├── README_coordinates.md ├── README_deploy.md ├── README_v1.md ├── UI_TARS_paper.pdf ├── UI_TARS_paper_preview.pdf ├── codes ├── .gitignore ├── .python-version ├── README.md ├── makefile ├── pyproject.toml ├── tests │ ├── action_parser_test.py │ └── inference_test.py ├── ui_tars │ ├── __init__.py │ ├── action_parser.py │ └── prompt.py └── uv.lock ├── data ├── coordinate_process_image.png ├── coordinate_process_image_som.png ├── test_messages.json ├── test_messages_single_image.json └── training_example.json └── figures ├── UI-TARS-vs-Previous-SOTA.png ├── UI-TARS.png ├── icon.png └── writer.png /.github/workflows/test.yml: -------------------------------------------------------------------------------- 1 | name: Test 2 | 3 | on: 4 | pull_request: 5 | types: [opened, synchronize, reopened] 6 | branches: 7 | - "**" 8 | push: 9 | branches: 10 | - "main" 11 | 12 | jobs: 13 | test_ui_tars: 14 | runs-on: ubuntu-latest 15 | steps: 16 | - uses: actions/checkout@v4 17 | - name: Set up Python 18 | uses: actions/setup-python@v5 19 | with: 20 | python-version: "3.11" 21 | - name: Install dependencies 22 | working-directory: codes 23 | run: | 24 | python -m pip install --upgrade pip uv 25 | uv sync 26 | - name: Run unit tests 27 | working-directory: codes 28 | run: | 29 | make test 30 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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8 |

9 | 🌐 Website   | 🤗 Hugging Face Models   10 | |    🔧 Deployment    |    📑 Paper    |   11 | 🖥️ UI-TARS-desktop  
🏄 Midscene (Browser Automation)    |   🫨 Discord   12 |

13 | 14 | [![](https://trendshift.io/api/badge/repositories/13561)](https://trendshift.io/repositories/13561) 15 |
16 | 17 | We also offer a **UI-TARS-desktop** version, which can operate on your **local personal device**. To use it, please visit [https://github.com/bytedance/UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop). To use UI-TARS in web automation, you may refer to the open-source project [Midscene.js](https://github.com/web-infra-dev/Midscene). 18 | **❗Notes**: Since Qwen 2.5vl based models ultilizes absolute coordinates to ground objects, please kindly refer to our illustration about how to process coordinates in this guide. 19 | 20 | ## Updates 21 | - 🌟 2025.04.16: We shared the latest progress of the UI-TARS-1.5 model in our [blog](https://seed-tars.com/1.5), which excels in playing games and performing GUI tasks, and we open-sourced the [UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B). 22 | - ✨ 2025.03.23: We updated the OSWorld inference scripts from the original official [OSWorld repository](https://github.com/xlang-ai/OSWorld/blob/main/run_uitars.py). Now, you can use the OSWorld official inference scripts to reproduce our results. 23 | 24 | ## Introduction 25 | 26 | UI-TARS-1.5, an open-source multimodal agent built upon a powerful vision-language model. It is capable of effectively performing diverse tasks within virtual worlds. 27 | 28 | Leveraging the foundational architecture introduced in [our recent paper](https://arxiv.org/abs/2501.12326), UI-TARS-1.5 integrates advanced reasoning enabled by reinforcement learning. This allows the model to reason through its thoughts before taking action, significantly enhancing its performance and adaptability, particularly in inference-time scaling. Our new 1.5 version achieves state-of-the-art results across a variety of standard benchmarks, demonstrating strong reasoning capabilities and notable improvements over prior models. 29 | 30 |

31 | 34 | 35 |

36 |

37 | 40 |

41 | 42 | ## 🚀 Quick Start Guide: Deploying and Using Our Model 43 | 44 | To help you get started quickly with our model, we recommend following the steps below in order. These steps will guide you through deployment, prediction post-processing to make the model take actions in your environment. 45 | 46 | 47 | ### ✅ Step 1: Deployment & Inference 48 | 49 | 👉 Deployment and Inference. 50 | This includes instructions for model deployment using huggingface endpoint, and running your first prediction. 51 | 52 | 53 | ### ✅ Step 2: Post Processing 54 | 55 | #### Installation 56 | ```bash 57 | pip install ui-tars 58 | # or 59 | uv pip install ui-tars 60 | ``` 61 | #### Usage 62 | ```python 63 | from ui_tars.action_parser import parse_action_to_structure_output, parsing_response_to_pyautogui_code 64 | 65 | response = "Thought: Click the button\nAction: click(start_box='(100,200)')" 66 | original_image_width, original_image_height = 1920, 1080 67 | parsed_dict = parse_action_to_structure_output( 68 | response, 69 | factor=1000, 70 | origin_resized_height=original_image_height, 71 | origin_resized_width=original_image_width, 72 | model_type="qwen25vl" 73 | ) 74 | print(parsed_dict) 75 | parsed_pyautogui_code = parsing_response_to_pyautogui_code( 76 | responses=parsed_dict, 77 | image_height=original_image_height, 78 | image_width=original_image_width 79 | ) 80 | print(parsed_pyautogui_code) 81 | ``` 82 | ##### FYI: Coordinates visualization 83 | To help you better understand the coordinate processing, we also provide a guide for coordinates processing visualization. 84 | 85 | ## Prompt Usage Guide 86 | 87 | To accommodate different device environments and task complexities, the following three prompt templates in codes/ui_tars/prompt.py. are designed to guide GUI agents in generating appropriate actions. Choose the template that best fits your use case: 88 | 89 | ### 🖥️ `COMPUTER_USE` 90 | 91 | **Recommended for**: GUI tasks on **desktop environments** such as Windows, Linux, or macOS. 92 | 93 | **Features**: 94 | - Supports common desktop operations: mouse clicks (single, double, right), drag actions, keyboard shortcuts, text input, scrolling, etc. 95 | - Ideal for browser navigation, office software interaction, file management, and other desktop-based tasks. 96 | 97 | 98 | ### 📱 `MOBILE_USE` 99 | 100 | **Recommended for**: GUI tasks on **mobile devices or Android emulators**. 101 | 102 | **Features**: 103 | - Includes mobile-specific actions: `long_press`, `open_app`, `press_home`, `press_back`. 104 | - Suitable for launching apps, scrolling views, filling input fields, and navigating within mobile apps. 105 | 106 | 107 | ### 📌 `GROUNDING` 108 | 109 | **Recommended for**: Lightweight tasks focused solely on **action output**, or for use in model training and evaluation. 110 | 111 | **Features**: 112 | - Only outputs the `Action` without any reasoning (`Thought`). 113 | - Useful for evaluating grounding capability. 114 | 115 | --- 116 | 117 | When developing or evaluating multimodal interaction systems, choose the appropriate prompt template based on your target platform (desktop vs. mobile) 118 | 119 | 120 | ## Performance 121 | **Online Benchmark Evaluation** 122 | | Benchmark type | Benchmark | UI-TARS-1.5 | OpenAI CUA | Claude 3.7 | Previous SOTA | 123 | |----------------|--------------------------------------------------------------------------------------------------------------------------------------------------|-------------|-------------|-------------|----------------------| 124 | | **Computer Use** | [OSworld](https://arxiv.org/abs/2404.07972) (100 steps) | **42.5** | 36.4 | 28 | 38.1 (200 step) | 125 | | | [Windows Agent Arena](https://arxiv.org/abs/2409.08264) (50 steps) | **42.1** | - | - | 29.8 | 126 | | **Browser Use** | [WebVoyager](https://arxiv.org/abs/2401.13919) | 84.8 | **87** | 84.1 | 87 | 127 | | | [Online-Mind2web](https://arxiv.org/abs/2504.01382) | **75.8** | 71 | 62.9 | 71 | 128 | | **Phone Use** | [Android World](https://arxiv.org/abs/2405.14573) | **64.2** | - | - | 59.5 | 129 | 130 | 131 | **Grounding Capability Evaluation** 132 | | Benchmark | UI-TARS-1.5 | OpenAI CUA | Claude 3.7 | Previous SOTA | 133 | |-----------|-------------|------------|------------|----------------| 134 | | [ScreenSpot-V2](https://arxiv.org/pdf/2410.23218) | **94.2** | 87.9 | 87.6 | 91.6 | 135 | | [ScreenSpotPro](https://arxiv.org/pdf/2504.07981v1) | **61.6** | 23.4 | 27.7 | 43.6 | 136 | 137 | 138 | 139 | **Poki Game** 140 | 141 | | Model | [2048](https://poki.com/en/g/2048) | [cubinko](https://poki.com/en/g/cubinko) | [energy](https://poki.com/en/g/energy) | [free-the-key](https://poki.com/en/g/free-the-key) | [Gem-11](https://poki.com/en/g/gem-11) | [hex-frvr](https://poki.com/en/g/hex-frvr) | [Infinity-Loop](https://poki.com/en/g/infinity-loop) | [Maze:Path-of-Light](https://poki.com/en/g/maze-path-of-light) | [shapes](https://poki.com/en/g/shapes) | [snake-solver](https://poki.com/en/g/snake-solver) | [wood-blocks-3d](https://poki.com/en/g/wood-blocks-3d) | [yarn-untangle](https://poki.com/en/g/yarn-untangle) | [laser-maze-puzzle](https://poki.com/en/g/laser-maze-puzzle) | [tiles-master](https://poki.com/en/g/tiles-master) | 142 | |-------------|-----------|--------------|-------------|-------------------|-------------|---------------|---------------------|--------------------------|-------------|--------------------|----------------------|---------------------|------------------------|---------------------| 143 | | OpenAI CUA | 31.04 | 0.00 | 32.80 | 0.00 | 46.27 | 92.25 | 23.08 | 35.00 | 52.18 | 42.86 | 2.02 | 44.56 | 80.00 | 78.27 | 144 | | Claude 3.7 | 43.05 | 0.00 | 41.60 | 0.00 | 0.00 | 30.76 | 2.31 | 82.00 | 6.26 | 42.86 | 0.00 | 13.77 | 28.00 | 52.18 | 145 | | UI-TARS-1.5 | 100.00 | 0.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 146 | 147 | 148 | **Minecraft** 149 | 150 | | Task Type | Task Name | [VPT](https://openai.com/index/vpt/) | [DreamerV3](https://www.nature.com/articles/s41586-025-08744-2) | Previous SOTA | UI-TARS-1.5 w/o Thought | UI-TARS-1.5 w/ Thought | 151 | |-------------|---------------------|----------|----------------|--------------------|------------------|-----------------| 152 | | Mine Blocks | (oak_log) | 0.8 | 1.0 | 1.0 | 1.0 | 1.0 | 153 | | | (obsidian) | 0.0 | 0.0 | 0.0 | 0.2 | 0.3 | 154 | | | (white_bed) | 0.0 | 0.0 | 0.1 | 0.4 | 0.6 | 155 | | | **200 Tasks Avg.** | 0.06 | 0.03 | 0.32 | 0.35 | 0.42 | 156 | | Kill Mobs | (mooshroom) | 0.0 | 0.0 | 0.1 | 0.3 | 0.4 | 157 | | | (zombie) | 0.4 | 0.1 | 0.6 | 0.7 | 0.9 | 158 | | | (chicken) | 0.1 | 0.0 | 0.4 | 0.5 | 0.6 | 159 | | | **100 Tasks Avg.** | 0.04 | 0.03 | 0.18 | 0.25 | 0.31 | 160 | 161 | ## Model Scale Comparison 162 | 163 | Here we compare performance across different model scales of UI-TARS on the OSworld benchmark. 164 | 165 | | **Benchmark Type** | **Benchmark** | **UI-TARS-72B-DPO** | **UI-TARS-1.5-7B** | **UI-TARS-1.5** | 166 | |--------------------|------------------------------------|---------------------|--------------------|-----------------| 167 | | Computer Use | [OSWorld](https://arxiv.org/abs/2404.07972) | 24.6 | 27.5 | **42.5** | 168 | | GUI Grounding | [ScreenSpotPro](https://arxiv.org/pdf/2504.07981v1) | 38.1 | 49.6 | **61.6** | 169 | 170 | ### Limitations 171 | 172 | While UI-TARS-1.5 represents a significant advancement in multimodal agent capabilities, we acknowledge several important limitations: 173 | 174 | - **Misuse:** Given its enhanced performance in GUI tasks, including successfully navigating authentication challenges like CAPTCHA, UI-TARS-1.5 could potentially be misused for unauthorized access or automation of protected content. To mitigate this risk, extensive internal safety evaluations are underway. 175 | - **Computation:** UI-TARS-1.5 still requires substantial computational resources, particularly for large-scale tasks or extended gameplay scenarios. 176 | - **Hallucination**: UI-TARS-1.5 may occasionally generate inaccurate descriptions, misidentify GUI elements, or take suboptimal actions based on incorrect inferences—especially in ambiguous or unfamiliar environments. 177 | - **Model scale:** The released UI-TARS-1.5-7B focuses primarily on enhancing general computer use capabilities and is not specifically optimized for game-based scenarios, where the UI-TARS-1.5 still holds a significant advantage. 178 | 179 | ## What's next 180 | 181 | We are providing early research access to our top-performing UI-TARS-1.5 model to facilitate collaborative research. Interested researchers can contact us at TARS@bytedance.com. 182 | 183 | Looking ahead, we envision UI-TARS evolving into increasingly sophisticated agentic experiences capable of performing real-world actions, thereby empowering platforms such as [doubao](https://team.doubao.com/en/) to accomplish more complex tasks for you :) 184 | 185 | ## Star History 186 | 187 | [![Star History Chart](https://api.star-history.com/svg?repos=bytedance/UI-TARS&type=Date)](https://www.star-history.com/#bytedance/UI-TARS&Date) 188 | 189 | ## Citation 190 | If you find our paper and model useful in your research, feel free to give us a cite. 191 | 192 | ```BibTeX 193 | @article{qin2025ui, 194 | title={UI-TARS: Pioneering Automated GUI Interaction with Native Agents}, 195 | author={Qin, Yujia and Ye, Yining and Fang, Junjie and Wang, Haoming and Liang, Shihao and Tian, Shizuo and Zhang, Junda and Li, Jiahao and Li, Yunxin and Huang, Shijue and others}, 196 | journal={arXiv preprint arXiv:2501.12326}, 197 | year={2025} 198 | } 199 | ``` 200 | -------------------------------------------------------------------------------- /README_coordinates.md: -------------------------------------------------------------------------------- 1 | 2 | # Tutorial: Processing Model Coordinate Outputs 3 | 4 | **Note**: For complete action space parsing, please refer to [OSWorld `uitars_agent.py`](https://github.com/xlang-ai/OSWorld/blob/main/mm_agents/uitars_agent.py). This tutorial assumes that we have already obtained the raw coordinate output from the model and will process it to determine the actual position in the image that the model intends to click. 5 | 6 | --- 7 | 8 | ## Steps: 9 | 10 | 1. **Visualize the Model's Output Coordinates** 11 | Use the code provided below to process the model's output and visualize the coordinates on the image. 12 | 13 | ## Code Example 14 | ```python 15 | # Assume model output 16 | model_raw_response = """Thought: xxx 17 | Action: click(start_box='(197,525)')""" 18 | 19 | # Please use re to parse the coordinate values 20 | model_output_width = 197 21 | model_output_height = 525 22 | 23 | from PIL import Image 24 | import matplotlib.pyplot as plt 25 | 26 | import json 27 | import base64 28 | from io import BytesIO 29 | from PIL import Image 30 | 31 | import math 32 | 33 | IMAGE_FACTOR = 28 34 | MIN_PIXELS = 100 * 28 * 28 35 | MAX_PIXELS = 16384 * 28 * 28 36 | MAX_RATIO = 200 37 | 38 | VIDEO_MIN_PIXELS = 128 * 28 * 28 39 | VIDEO_MAX_PIXELS = 768 * 28 * 28 40 | FRAME_FACTOR = 2 41 | FPS = 2.0 42 | FPS_MIN_FRAMES = 4 43 | FPS_MAX_FRAMES = 768 44 | 45 | def round_by_factor(number: int, factor: int) -> int: 46 | """Returns the closest integer to 'number' that is divisible by 'factor'.""" 47 | return round(number / factor) * factor 48 | 49 | def ceil_by_factor(number: int, factor: int) -> int: 50 | """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" 51 | return math.ceil(number / factor) * factor 52 | 53 | def floor_by_factor(number: int, factor: int) -> int: 54 | """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" 55 | return math.floor(number / factor) * factor 56 | 57 | def smart_resize( 58 | height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS 59 | ) -> tuple[int, int]: 60 | """ 61 | Rescales the image so that the following conditions are met: 62 | 63 | 1. Both dimensions (height and width) are divisible by 'factor'. 64 | 65 | 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 66 | 67 | 3. The aspect ratio of the image is maintained as closely as possible. 68 | """ 69 | if max(height, width) / min(height, width) > MAX_RATIO: 70 | raise ValueError( 71 | f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" 72 | ) 73 | h_bar = max(factor, round_by_factor(height, factor)) 74 | w_bar = max(factor, round_by_factor(width, factor)) 75 | if h_bar * w_bar > max_pixels: 76 | beta = math.sqrt((height * width) / max_pixels) 77 | h_bar = floor_by_factor(height / beta, factor) 78 | w_bar = floor_by_factor(width / beta, factor) 79 | elif h_bar * w_bar < min_pixels: 80 | beta = math.sqrt(min_pixels / (height * width)) 81 | h_bar = ceil_by_factor(height * beta, factor) 82 | w_bar = ceil_by_factor(width * beta, factor) 83 | return h_bar, w_bar 84 | 85 | # Open the image 86 | img = Image.open('./data/coordinate_process_image.png') 87 | width, height = img.size 88 | print(f'Original resolution: {width}, {height}') 89 | # Calculate the new dimensions 90 | new_height, new_width = smart_resize(height, width) 91 | new_coordinate = (int(model_output_width/new_width * width), int(model_output_height/new_height * height)) 92 | print(f'Resized resolution: {new_width}, {new_height}') 93 | print(new_coordinate) 94 | 95 | # Display the image 96 | plt.imshow(img) 97 | plt.scatter([new_coordinate[0]], [new_coordinate[1]], c='red', s=50) # Mark the point with a red dot 98 | plt.title('Visualize Coordinate') 99 | plt.axis('off') # Set to 'off' to hide the axes 100 | plt.savefig('./data/coordinate_process_image_som.png', dpi=350) 101 | ``` 102 | 103 | 2. The output SOM image should look like this: 104 | 105 | ![Output SOM Image](./data/coordinate_process_image_som.png) 106 | -------------------------------------------------------------------------------- /README_deploy.md: -------------------------------------------------------------------------------- 1 | # UI-TARS 1.5 HuggingFace Endpoint Deployment Guide 2 | 3 | ## 1. HuggingFace Inference Endpoints Cloud Deployment 4 | 5 | We use HuggingFace's Inference Endpoints platform to quickly deploy a cloud-based model. 6 | 7 | ### Deployment Steps 8 | 9 | 1. **Access the Deployment Interface** 10 | - Click [Deploy from Hugging Face](https://endpoints.huggingface.co/catalog) 11 | ![Deploy from Hugging Face](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_1_formal.png?download=true) 12 | - Select the model `UI-TARS 1.5 7B` and click **Import Model** 13 | ![Import Model](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_2_formal.png?download=true) 14 | 15 | 2. **Configure Settings** 16 | - **Hardware Configuration** 17 | - In the `Hardware Configuration` section, choose a GPU instance. Here are the recommendations for different model sizes: 18 | - For the 7B model, select `GPU L40S 1GPU 48G` (Recommended: Nvidia L4 / Nvidia A100). 19 | ![Hardware Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_3_formal.png?download=true) 20 | 21 | - **Container Configuration** 22 | - Set the following parameters: 23 | - `Max Input Length (per Query)`: 65536 24 | - `Max Batch Prefill Tokens`: 65536 25 | - `Max Number of Tokens (per Query)`: 65537 26 | ![Container Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_4_formal.png?download=true) 27 | 28 | - **Environment Variables** 29 | - Add the following environment variables: 30 | - `CUDA_GRAPHS=0` to avoid deployment failures. For details, refer to [issue 2875](https://github.com/huggingface/text-generation-inference/issues/2875). 31 | - `PAYLOAD_LIMIT=8000000` to prevent request failures due to large images. For details, refer to [issue 1802](https://github.com/huggingface/text-generation-inference/issues/1802). 32 | ![Environment Variables](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_5_formal.png?download=true) 33 | 34 | - **Create Endpoint** 35 | - Click **Create** to set up the endpoint. 36 | ![Create Endpoint](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_6_formal.png?download=true) 37 | 38 | - **Enter Setup** 39 | - Once the deployment is finished, you will see the confirmation page and need to enter the settings page. 40 | ![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_7_formal.png?download=true) 41 | 42 | - **Update URI** - 43 | - Go to the Container page, set the Container URI to ghcr.io/huggingface/text-generation-inference:3.2.1, and **click Update Endpoint to apply the changes**. 44 | ![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_8_formal.png?download=true) 45 | 46 | 47 | ## 2. API Usage Example 48 | 49 | ### **Python Test Code** 50 | ```python 51 | # pip install openai 52 | import io 53 | import re 54 | import json 55 | import base64 56 | from PIL import Image 57 | from io import BytesIO 58 | from openai import OpenAI 59 | 60 | def add_box_token(input_string): 61 | # Step 1: Split the string into individual actions 62 | if "Action: " in input_string and "start_box=" in input_string: 63 | suffix = input_string.split("Action: ")[0] + "Action: " 64 | actions = input_string.split("Action: ")[1:] 65 | processed_actions = [] 66 | for action in actions: 67 | action = action.strip() 68 | # Step 2: Extract coordinates (start_box or end_box) using regex 69 | coordinates = re.findall(r"(start_box|end_box)='\((\d+),\s*(\d+)\)'", action) 70 | 71 | updated_action = action # Start with the original action 72 | for coord_type, x, y in coordinates: 73 | # Convert x and y to integers 74 | updated_action = updated_action.replace(f"{coord_type}='({x},{y})'", f"{coord_type}='<|box_start|>({x},{y})<|box_end|>'") 75 | processed_actions.append(updated_action) 76 | 77 | # Step 5: Reconstruct the final string 78 | final_string = suffix + "\n\n".join(processed_actions) 79 | else: 80 | final_string = input_string 81 | return final_string 82 | 83 | client = OpenAI( 84 | base_url="https:xxx", 85 | api_key="hf_xxx" 86 | ) 87 | 88 | result = {} 89 | messages = json.load(open("./data/test_messages.json")) 90 | for message in messages: 91 | if message["role"] == "assistant": 92 | message["content"] = add_box_token(message["content"]) 93 | print(message["content"]) 94 | 95 | chat_completion = client.chat.completions.create( 96 | model="tgi", 97 | messages=messages, 98 | top_p=None, 99 | temperature=0.0, 100 | max_tokens=400, 101 | stream=True, 102 | seed=None, 103 | stop=None, 104 | frequency_penalty=None, 105 | presence_penalty=None 106 | ) 107 | 108 | response = "" 109 | for message in chat_completion: 110 | response += message.choices[0].delta.content 111 | print(response) 112 | ``` 113 | 114 | ### **Expected Output** ### 115 | ```python 116 | Thought: 我看到Preferences窗口已经打开了，但这里显示的都是系统资源相关的设置。要设置图片的颜色模式，我得先看看左侧的选项列表。嗯，"Color Management"这个选项看起来很有希望，应该就是处理颜色管理的地方。让我点击它看看里面有什么选项。 117 | Action: click(start_box='(177,549)') 118 | ``` 119 | -------------------------------------------------------------------------------- /README_v1.md: -------------------------------------------------------------------------------- 1 | 6 | ![Local Image](figures/writer.png) 7 |

8 | 🤗 Hugging Face Models   |   🤖 ModelScope   |    📑 Paper    |   9 | 🖥️ UI-TARS-desktop  
🏄 Midscene (Browser Automation)    |   🤗 Space   |   🫨 Discord   10 |

11 | 12 | We also offer a **UI-TARS-desktop** version, which can operate on your **local personal device**. To use it, please visit [https://github.com/bytedance/UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop). To use UI-TARS in web automation, you may refer to the open-source project [Midscene.js](https://github.com/web-infra-dev/Midscene). 13 | ### ⚠️ Important Announcement: GGUF Model Performance 14 | 15 | The **GGUF model** has undergone quantization, but unfortunately, its performance cannot be guaranteed. As a result, we have decided to **downgrade** it. 16 | 17 | 💡 **Alternative Solution**: 18 | You can use **[Cloud Deployment](#cloud-deployment)** or **[Local Deployment [vLLM]](#local-deployment-vllm)**(If you have enough GPU resources) instead. 19 | 20 | We appreciate your understanding and patience as we work to ensure the best possible experience. 21 | 22 | ## Updates 23 | - ✨ We updated the OSWorld inference scripts from the original official [OSWorld repository](https://github.com/xlang-ai/OSWorld/blob/main/run_uitars.py). Now, you can use the OSWorld official inference scripts for deployment and we've provided [trajectory examples](https://drive.google.com/file/d/1N9dYzAB9xSiHwE9VSdEi9xSpB9eXfVZT/view?usp=sharing) for OSWorld to help you get started. 24 | - 🚀 01.25: We updated the **[Cloud Deployment](#cloud-deployment)** section in the 中文版: [GUI模型部署教程](https://bytedance.sg.larkoffice.com/docx/TCcudYwyIox5vyxiSDLlgIsTgWf#U94rdCxzBoJMLex38NPlHL21gNb) with new information related to the ModelScope platform. You can now use the ModelScope platform for deployment. 25 | 26 | 27 | 28 | ## Overview 29 | UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components—perception, reasoning, grounding, and memory—within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules. 30 | ![Local Image](figures/UI-TARS-vs-Previous-SOTA.png) 31 | ![Local Image](figures/UI-TARS.png) 32 | 33 | 34 | ## Core Features 35 | ### Perception 36 | - **Comprehensive GUI Understanding**: Processes multimodal inputs (text, images, interactions) to build a coherent understanding of interfaces. 37 | - **Real-Time Interaction**: Continuously monitors dynamic GUIs and responds accurately to changes in real-time. 38 | 39 | ### Action 40 | - **Unified Action Space**: Standardized action definitions across platforms (desktop, mobile, and web). 41 | - **Platform-Specific Actions**: Supports additional actions like hotkeys, long press, and platform-specific gestures. 42 | 43 | ### Reasoning 44 | - **System 1 & System 2 Reasoning**: Combines fast, intuitive responses with deliberate, high-level planning for complex tasks. 45 | - **Task Decomposition & Reflection**: Supports multi-step planning, reflection, and error correction for robust task execution. 46 | 47 | ### Memory 48 | - **Short-Term Memory**: Captures task-specific context for situational awareness. 49 | - **Long-Term Memory**: Retains historical interactions and knowledge for improved decision-making. 50 | 51 | ## Capabilities 52 | - **Cross-Platform Interaction**: Supports desktop, mobile, and web environments with a unified action framework. 53 | - **Multi-Step Task Execution**: Trained to handle complex tasks through multi-step trajectories and reasoning. 54 | - **Learning from Synthetic and Real Data**: Combines large-scale annotated and synthetic datasets for improved generalization and robustness. 55 | 56 | ## Performance 57 | **Perception Capabilty Evaluation** 58 | | Model | VisualWebBench | WebSRC | SQAshort | 59 | |---------------------------|---------------|---------|----------| 60 | | Qwen2-VL-7B | 73.3 | 81.8 | 84.9 | 61 | | Qwen-VL-Max | 74.1 | 91.1 | 78.6 | 62 | | Gemini-1.5-Pro | 75.4 | 88.9 | 82.2 | 63 | | UIX-Qwen2-7B | 75.9 | 82.9 | 78.8 | 64 | | Claude-3.5-Sonnet | 78.2 | 90.4 | 83.1 | 65 | | GPT-4o | 78.5 | 87.7 | 82.3 | 66 | | **UI-TARS-2B** | 72.9 | 89.2 | 86.4 | 67 | | **UI-TARS-7B** | 79.7 | **93.6** | 87.7 | 68 | | **UI-TARS-72B** | **82.8** | 89.3 | **88.6** | 69 | 70 | **Grounding Capability Evaluation** 71 | - **ScreenSpot Pro** 72 | 73 | | Agent Model | Dev-Text | Dev-Icon | Dev-Avg | Creative-Text | Creative-Icon | Creative-Avg | CAD-Text | CAD-Icon | CAD-Avg | Scientific-Text | Scientific-Icon | Scientific-Avg | Office-Text | Office-Icon | Office-Avg | OS-Text | OS-Icon | OS-Avg | Avg-Text | Avg-Icon | Avg | 74 | |--------------------------|----------|----------|----------|--------------|--------------|--------------|---------|---------|---------|---------------|---------------|---------------|------------|------------|------------|--------|--------|--------|---------|---------|------| 75 | | QwenVL-7B | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | **0.1** | 76 | | GPT-4o | 1.3 | 0.0 | 0.7 | 1.0 | 0.0 | 0.6 | 2.0 | 0.0 | 1.5 | 2.1 | 0.0 | 1.2 | 1.1 | 0.0 | 0.9 | 0.0 | 0.0 | 0.0 | 1.3 | 0.0 | **0.8** | 77 | | SeeClick | 0.6 | 0.0 | 0.3 | 1.0 | 0.0 | 0.6 | 2.5 | 0.0 | 1.9 | 3.5 | 0.0 | 2.0 | 1.1 | 0.0 | 0.9 | 2.8 | 0.0 | 1.5 | 1.8 | 0.0 | **1.1** | 78 | | Qwen2-VL-7B | 2.6 | 0.0 | 1.3 | 1.5 | 0.0 | 0.9 | 0.5 | 0.0 | 0.4 | 6.3 | 0.0 | 3.5 | 3.4 | 1.9 | 3.0 | 0.9 | 0.0 | 0.5 | 2.5 | 0.2 | **1.6** | 79 | | OS-Atlas-4B | 7.1 | 0.0 | 3.7 | 3.0 | 1.4 | 2.3 | 2.0 | 0.0 | 1.5 | 9.0 | 5.5 | 7.5 | 5.1 | 3.8 | 4.8 | 5.6 | 0.0 | 3.1 | 5.0 | 1.7 | **3.7** | 80 | | ShowUI-2B | 16.9 | 1.4 | 9.4 | 9.1 | 0.0 | 5.3 | 2.5 | 0.0 | 1.9 | 13.2 | 7.3 | 10.6 | 15.3 | 7.5 | 13.5 | 10.3 | 2.2 | 6.6 | 10.8 | 2.6 | **7.7** | 81 | | CogAgent-18B | 14.9 | 0.7 | 8.0 | 9.6 | 0.0 | 5.6 | 7.1 | 3.1 | 6.1 | 22.2 | 1.8 | 13.4 | 13.0 | 0.0 | 10.0 | 5.6 | 0.0 | 3.1 | 12.0 | 0.8 | **7.7** | 82 | | Aria-UI | 16.2 | 0.0 | 8.4 | 23.7 | 2.1 | 14.7 | 7.6 | 1.6 | 6.1 | 27.1 | 6.4 | 18.1 | 20.3 | 1.9 | 16.1 | 4.7 | 0.0 | 2.6 | 17.1 | 2.0 | **11.3** | 83 | | UGround-7B | 26.6 | 2.1 | 14.7 | 27.3 | 2.8 | 17.0 | 14.2 | 1.6 | 11.1 | 31.9 | 2.7 | 19.3 | 31.6 | 11.3 | 27.0 | 17.8 | 0.0 | 9.7 | 25.0 | 2.8 | **16.5** | 84 | | Claude Computer Use | 22.0 | 3.9 | 12.6 | 25.9 | 3.4 | 16.8 | 14.5 | 3.7 | 11.9 | 33.9 | 15.8 | 25.8 | 30.1 | 16.3 | 26.9 | 11.0 | 4.5 | 8.1 | 23.4 | 7.1 | **17.1** | 85 | | OS-Atlas-7B | 33.1 | 1.4 | 17.7 | 28.8 | 2.8 | 17.9 | 12.2 | 4.7 | 10.3 | 37.5 | 7.3 | 24.4 | 33.9 | 5.7 | 27.4 | 27.1 | 4.5 | 16.8 | 28.1 | 4.0 | **18.9** | 86 | | UGround-V1-7B | - | - | 35.5 | - | - | 27.8 | - | - | 13.5 | - | - | 38.8 | - | - | 48.8 | - | - | 26.1 | - | - | **31.1** | 87 | | **UI-TARS-2B** | 47.4 | 4.1 | 26.4 | 42.9 | 6.3 | 27.6 | 17.8 | 4.7 | 14.6 | 56.9 | 17.3 | 39.8 | 50.3 | 17.0 | 42.6 | 21.5 | 5.6 | 14.3 | 39.6 | 8.4 | **27.7** | 88 | | **UI-TARS-7B** | 58.4 | 12.4 | 36.1 | 50.0 | 9.1 | 32.8 | **20.8**| 9.4 | **18.0**| 63.9 | **31.8** | **50.0** | **63.3** | 20.8 | 53.5 | 30.8 | **16.9**| 24.5 | 47.8 | 16.2 | **35.7** | 89 | | **UI-TARS-72B** | **63.0** | **17.3** | **40.8** | **57.1** | **15.4** | **39.6** | 18.8 | **12.5**| 17.2 | **64.6** | 20.9 | 45.7 | **63.3** | **26.4** | **54.8** | **42.1**| 15.7 | **30.1**| **50.9**| **17.5**| **38.1** | 90 | 91 | 92 | - **ScreenSpot** 93 | 94 | | Method | Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg | 95 | |--------|-------------|-------------|-------------|-------------|-------------|---------|---------| 96 | | **Agent Framework** | | | | | | | | 97 | | GPT-4 (SeeClick) | 76.6 | 55.5 | 68.0 | 28.6 | 40.9 | 23.3 | **48.8** | 98 | | GPT-4 (OmniParser) | 93.9 | 57.0 | 91.3 | 63.6 | 81.3 | 51.0 | **73.0** | 99 | | GPT-4 (UGround-7B) | 90.1 | 70.3 | 87.1 | 55.7 | 85.7 | 64.6 | **75.6** | 100 | | GPT-4o (SeeClick) | 81.0 | 59.8 | 69.6 | 33.6 | 43.9 | 26.2 | **52.3** | 101 | | GPT-4o (UGround-7B) | 93.4 | 76.9 | 92.8 | 67.9 | 88.7 | 68.9 | **81.4** | 102 | | **Agent Model** | | | | | | | | 103 | | GPT-4 | 22.6 | 24.5 | 20.2 | 11.8 | 9.2 | 8.8 | **16.2** | 104 | | GPT-4o | 20.2 | 24.9 | 21.1 | 23.6 | 12.2 | 7.8 | **18.3** | 105 | | CogAgent | 67.0 | 24.0 | 74.2 | 20.0 | 70.4 | 28.6 | **47.4** | 106 | | SeeClick | 78.0 | 52.0 | 72.2 | 30.0 | 55.7 | 32.5 | **53.4** | 107 | | Qwen2-VL | 75.5 | 60.7 | 76.3 | 54.3 | 35.2 | 25.7 | **55.3** | 108 | | UGround-7B | 82.8 | 60.3 | 82.5 | 63.6 | 80.4 | 70.4 | **73.3** | 109 | | Aguvis-G-7B | 88.3 | 78.2 | 88.1 | 70.7 | 85.7 | 74.8 | **81.8** | 110 | | OS-Atlas-7B | 93.0 | 72.9 | 91.8 | 62.9 | 90.9 | 74.3 | **82.5** | 111 | | Claude Computer Use | - | - | - | - | - | - | **83.0** | 112 | | Gemini 2.0 (Project Mariner) | - | - | - | - | - | - | **84.0** | 113 | | Aguvis-7B | **95.6** | 77.7 | 93.8 | 67.1 | 88.3 | 75.2 | **84.4** | 114 | | Aguvis-72B | 94.5 | **85.2** | 95.4 | 77.9 | **91.3** | **85.9** | **89.2** | 115 | | **Our Model** | | | | | | | | 116 | | **UI-TARS-2B** | 93.0 | 75.5 | 90.7 | 68.6 | 84.3 | 74.8 | **82.3** | 117 | | **UI-TARS-7B** | 94.5 | **85.2** | **95.9** | 85.7 | 90.0 | 83.5 | **89.5** | 118 | | **UI-TARS-72B** | 94.9 | 82.5 | 89.7 | **88.6** | 88.7 | 85.0 | **88.4** | 119 | 120 | 121 | - **ScreenSpot v2** 122 | 123 | | Method | Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg | 124 | |--------|-------------|-------------|-------------|-------------|-------------|---------|---------| 125 | | **Agent Framework** | | | | | | | | 126 | | GPT-4o (SeeClick) | 85.2 | 58.8 | 79.9 | 37.1 | 72.7 | 30.1 | **63.6** | 127 | | GPT-4o (OS-Atlas-4B) | 95.5 | 75.8 | 79.4 | 49.3 | 90.2 | 66.5 | **79.1** | 128 | | GPT-4o (OS-Atlas-7B) | 96.2 | 83.4 | 89.7 | 69.3 | **94.0** | 79.8 | **87.1** | 129 | | **Agent Model** | | | | | | | | 130 | | SeeClick | 78.4 | 50.7 | 70.1 | 29.3 | 55.2 | 32.5 | **55.1** | 131 | | OS-Atlas-4B | 87.2 | 59.7 | 72.7 | 46.4 | 85.9 | 63.1 | **71.9** | 132 | | OS-Atlas-7B | 95.2 | 75.8 | 90.7 | 63.6 | 90.6 | 77.3 | **84.1** | 133 | | **Our Model** | | | | | | | | 134 | | **UI-TARS-2B** | 95.2 | 79.1 | 90.7 | 68.6 | 87.2 | 78.3 | **84.7** | 135 | | **UI-TARS-7B** | **96.9** | **89.1** | **95.4** | 85.0 | 93.6 | 85.2 | **91.6** | 136 | | **UI-TARS-72B** | 94.8 | 86.3 | 91.2 | **87.9** | 91.5 | **87.7** | **90.3** | 137 | 138 | 139 | **Offline Agent Capability Evaluation** 140 | - **Multimodal Mind2Web** 141 | 142 | | Method | Cross-Task Ele.Acc | Cross-Task Op.F1 | Cross-Task Step SR | Cross-Website Ele.Acc | Cross-Website Op.F1 | Cross-Website Step SR | Cross-Domain Ele.Acc | Cross-Domain Op.F1 | Cross-Domain Step SR | 143 | |--------|----------------------|-------------------|--------------------|----------------------|--------------------|-------------------|--------------------|-------------------|-------------------| 144 | | **Agent Framework** | | | | | | | | | | 145 | | GPT-4o (SeeClick) | 32.1 | - | - | 33.1 | - | - | 33.5 | - | - | 146 | | GPT-4o (UGround) | 47.7 | - | - | 46.0 | - | - | 46.6 | - | - | 147 | | GPT-4o (Aria-UI) | 57.6 | - | - | 57.7 | - | - | 61.4 | - | - | 148 | | GPT-4V (OmniParser) | 42.4 | 87.6 | 39.4 | 41.0 | 84.8 | 36.5 | 45.5 | 85.7 | 42.0 | 149 | | **Agent Model** | | | | | | | | | | 150 | | GPT-4o | 5.7 | 77.2 | 4.3 | 5.7 | 79.0 | 3.9 | 5.5 | 86.4 | 4.5 | 151 | | GPT-4 (SOM) | 29.6 | - | 20.3 | 20.1 | - | 13.9 | 27.0 | - | 23.7 | 152 | | GPT-3.5 (Text-only) | 19.4 | 59.2 | 16.8 | 14.9 | 56.5 | 14.1 | 25.2 | 57.9 | 24.1 | 153 | | GPT-4 (Text-only) | 40.8 | 63.1 | 32.3 | 30.2 | 61.0 | 27.0 | 35.4 | 61.9 | 29.7 | 154 | | Claude | 62.7 | 84.7 | 53.5 | 59.5 | 79.6 | 47.7 | 64.5 | 85.4 | 56.4 | 155 | | Aguvis-7B | 64.2 | 89.8 | 60.4 | 60.7 | 88.1 | 54.6 | 60.4 | 89.2 | 56.6 | 156 | | CogAgent | - | - | 62.3 | - | - | 54.0 | - | - | 59.4 | 157 | | Aguvis-72B | 69.5 | 90.8 | 64.0 | 62.6 | 88.6 | 56.5 | 63.5 | 88.5 | 58.2 | 158 | | **Our Model** | | | | | | | | | | 159 | | **UI-TARS-2B** | 62.3 | 90.0 | 56.3 | 58.5 | 87.2 | 50.8 | 58.8 | 89.6 | 52.3 | 160 | | **UI-TARS-7B** | 73.1 | 92.2 | 67.1 | 68.2 | 90.9 | 61.7 | 66.6 | 90.9 | 60.5 | 161 | | **UI-TARS-72B** | **74.7** | **92.5** | **68.6** | **72.4** | **91.2** | **63.5** | **68.9** | **91.8** | **62.1** | 162 | 163 | 164 | - **Android Control and GUI Odyssey** 165 | 166 | | Agent Models | AndroidControl-Low Type | AndroidControl-Low Grounding | AndroidControl-Low SR | AndroidControl-High Type | AndroidControl-High Grounding | AndroidControl-High SR | GUIOdyssey Type | GUIOdyssey Grounding | GUIOdyssey SR | 167 | |---------------------|----------------------|----------------------|----------------|----------------------|----------------------|----------------|----------------|----------------|----------------| 168 | | Claude | 74.3 | 0.0 | 19.4 | 63.7 | 0.0 | 12.5 | 60.9 | 0.0 | 3.1 | 169 | | GPT-4o | 74.3 | 0.0 | 19.4 | 66.3 | 0.0 | 20.8 | 34.3 | 0.0 | 3.3 | 170 | | SeeClick | 93.0 | 73.4 | 75.0 | 82.9 | 62.9 | 59.1 | 71.0 | 52.4 | 53.9 | 171 | | InternVL-2-4B | 90.9 | 84.1 | 80.1 | 84.1 | 72.7 | 66.7 | 82.1 | 55.5 | 51.5 | 172 | | Qwen2-VL-7B | 91.9 | 86.5 | 82.6 | 83.8 | 77.7 | 69.7 | 83.5 | 65.9 | 60.2 | 173 | | Aria-UI | -- | 87.7 | 67.3 | -- | 43.2 | 10.2 | -- | 86.8 | 36.5 | 174 | | OS-Atlas-4B | 91.9 | 83.8 | 80.6 | 84.7 | 73.8 | 67.5 | 83.5 | 61.4 | 56.4 | 175 | | OS-Atlas-7B | 93.6 | 88.0 | 85.2 | 85.2 | 78.5 | 71.2 | 84.5 | 67.8 | 62.0 | 176 | | Aguvis-7B | -- | -- | 80.5 | -- | -- | 61.5 | -- | -- | -- | 177 | | Aguvis-72B | -- | -- | 84.4 | -- | -- | 66.4 | -- | -- | -- | 178 | | **UI-TARS-2B** | **98.1** | 87.3 | 89.3 | 81.2 | 78.4 | 68.9 | 93.9 | 86.8 | 83.4 | 179 | | **UI-TARS-7B** | 98.0 | 89.3 | 90.8 | 83.7 | 80.5 | 72.5 | 94.6 | 90.1 | 87.0 | 180 | | **UI-TARS-72B** | **98.1** | **89.9** | **91.3** | **85.2** | **81.5** | **74.7** | **95.4** | **91.4** | **88.6** | 181 | 182 | **Online Agent Capability Evaluation** 183 | 184 | | Method | OSWorld (Online) | AndroidWorld (Online) | 185 | |--------|-------------------|------------------| 186 | | **Agent Framework** | | | 187 | | GPT-4o (UGround) | - | 32.8 | 188 | | GPT-4o (Aria-UI) | 15.2 | 44.8 | 189 | | GPT-4o (Aguvis-7B) | 14.8 | 37.1 | 190 | | GPT-4o (Aguvis-72B) | 17.0 | - | 191 | | GPT-4o (OS-Atlas-7B) | 14.6 | - | 192 | | **Agent Model** | | | 193 | | GPT-4o | 5.0 | 34.5 (SoM) | 194 | | Gemini-Pro-1.5 | 5.4 | 22.8 (SoM) | 195 | | Aguvis-72B | 10.3 | 26.1 | 196 | | Claude Computer-Use | 14.9 (15 steps) | 27.9 | 197 | | Claude Computer-Use | 22.0 (50 steps) | - | 198 | | **Our Model** | | | 199 | | **UI-TARS-7B-SFT** | 17.7 (15 steps) | 33.0 | 200 | | **UI-TARS-7B-DPO** | 18.7 (15 steps) | - | 201 | | **UI-TARS-72B-SFT** | 18.8 (15 steps) | **46.6** | 202 | | **UI-TARS-72B-DPO** | **22.7** (15 steps) | - | 203 | | **UI-TARS-72B-DPO** | **24.6** (50 steps) | - | 204 | 205 | ## Deployment 206 | 207 | ### Cloud Deployment 208 | We recommend using HuggingFace Inference Endpoints for fast deployment. 209 | We provide two docs for reference: 210 | 211 | English version: [GUI Model Deployment Guide](https://juniper-switch-f10.notion.site/GUI-Model-Deployment-Guide-17b5350241e280058e98cea60317de71) 212 | 213 | 中文版: [GUI模型部署教程](https://bytedance.sg.larkoffice.com/docx/TCcudYwyIox5vyxiSDLlgIsTgWf#U94rdCxzBoJMLex38NPlHL21gNb) 214 | 215 | ### Local Deployment [Transformers] 216 | We follow the same way as Qwen2-VL. Check this [tutorial](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#using---transformers-to-chat) for more details. 217 | 218 | ### Local Deployment [vLLM] 219 | We recommend using vLLM for fast deployment and inference. You need to use `vllm>=0.6.1`. 220 | ```bash 221 | pip install -U transformers 222 | VLLM_VERSION=0.6.6 223 | CUDA_VERSION=cu124 224 | pip install vllm==${VLLM_VERSION} --extra-index-url https://download.pytorch.org/whl/${CUDA_VERSION} 225 | 226 | ``` 227 | #### Download the Model 228 | We provide three model sizes on Hugging Face: **2B**, **7B**, and **72B**. To achieve the best performance, we recommend using the **7B-DPO** or **72B-DPO** model (depends on your GPU configuration): 229 | 230 | - [2B-SFT](https://huggingface.co/bytedance-research/UI-TARS-2B-SFT) 231 | - [7B-SFT](https://huggingface.co/bytedance-research/UI-TARS-7B-SFT) 232 | - [7B-DPO](https://huggingface.co/bytedance-research/UI-TARS-7B-DPO) 233 | - [72B-SFT](https://huggingface.co/bytedance-research/UI-TARS-72B-SFT) 234 | - [72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO) 235 | 236 | 237 | #### Start an OpenAI API Service 238 | Run the command below to start an OpenAI-compatible API service. It is recommended to set the tensor parallel size `-tp=1` for 7B models and `-tp=4` for 72B models. 239 | 240 | ```bash 241 | python -m vllm.entrypoints.openai.api_server --served-model-name ui-tars \ 242 | --model --limit-mm-per-prompt image=5 -tp 243 | ``` 244 | 245 | Then you can use the chat API as below with the gui prompt (choose from mobile or computer) and base64-encoded local images (see [OpenAI API protocol document](https://platform.openai.com/docs/guides/vision/uploading-base-64-encoded-images) for more details), you can also use it in [UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop): 246 | ```python 247 | import base64 248 | from openai import OpenAI 249 | 250 | 251 | instruction = "search for today's weather" 252 | screenshot_path = "screenshot.png" 253 | client = OpenAI( 254 | base_url="http://127.0.0.1:8000/v1", 255 | api_key="empty", 256 | ) 257 | 258 | ## Below is the prompt for mobile 259 | prompt = r"""You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 260 | 261 | ## Output Format 262 | ```\nThought: ... 263 | Action: ...\n``` 264 | 265 | ## Action Space 266 | 267 | click(start_box='<|box_start|>(x1,y1)<|box_end|>') 268 | left_double(start_box='<|box_start|>(x1,y1)<|box_end|>') 269 | right_single(start_box='<|box_start|>(x1,y1)<|box_end|>') 270 | drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') 271 | hotkey(key='') 272 | type(content='') #If you want to submit your input, use \"\ 273 | \" at the end of `content`. 274 | scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left') 275 | wait() #Sleep for 5s and take a screenshot to check for any changes. 276 | finished() 277 | call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help. 278 | 279 | 280 | ## Note 281 | - Use Chinese in `Thought` part. 282 | - Summarize your next action (with its target element) in one sentence in `Thought` part. 283 | 284 | ## User Instruction 285 | """ 286 | 287 | with open(screenshot_path, "rb") as image_file: 288 | encoded_string = base64.b64encode(image_file.read()).decode("utf-8") 289 | response = client.chat.completions.create( 290 | model="ui-tars", 291 | messages=[ 292 | { 293 | "role": "user", 294 | "content": [ 295 | {"type": "text", "text": prompt + instruction}, 296 | {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}, 297 | ], 298 | }, 299 | ], 300 | frequency_penalty=1, 301 | max_tokens=128, 302 | ) 303 | print(response.choices[0].message.content) 304 | ``` 305 | 306 | For single step grounding task or inference on grounding dataset such as Seeclick, kindly refer to the following script: 307 | ```python 308 | import base64 309 | from openai import OpenAI 310 | 311 | 312 | instruction = "search for today's weather" 313 | screenshot_path = "screenshot.png" 314 | client = OpenAI( 315 | base_url="http://127.0.0.1:8000/v1", 316 | api_key="empty", 317 | ) 318 | 319 | ## Below is the prompt for mobile 320 | prompt = r"""Output only the coordinate of one point in your response. What element matches the following task: """ 321 | 322 | with open(screenshot_path, "rb") as image_file: 323 | encoded_string = base64.b64encode(image_file.read()).decode("utf-8") 324 | response = client.chat.completions.create( 325 | model="ui-tars", 326 | messages=[ 327 | { 328 | "role": "user", 329 | "content": [ 330 | {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}, 331 | {"type": "text", "text": prompt + instruction} 332 | ], 333 | }, 334 | ], 335 | frequency_penalty=1, 336 | max_tokens=128, 337 | ) 338 | print(response.choices[0].message.content) 339 | ``` 340 | 341 | ### Prompt Templates 342 | We provide two prompt templates currently for stable running and performance, one for mobile scene and one for personal computer scene. 343 | - Prompt template for mobile: 344 | ```python 345 | ## Below is the prompt for mobile 346 | prompt = r"""You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 347 | 348 | ## Output Format 349 | ```\nThought: ... 350 | Action: ...\n``` 351 | 352 | ## Action Space 353 | click(start_box='<|box_start|>(x1,y1)<|box_end|>') 354 | long_press(start_box='<|box_start|>(x1,y1)<|box_end|>', time='') 355 | type(content='') 356 | scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') 357 | press_home() 358 | press_back() 359 | finished(content='') # Submit the task regardless of whether it succeeds or fails. 360 | 361 | ## Note 362 | - Use English in `Thought` part. 363 | 364 | - Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part. 365 | 366 | ## User Instruction 367 | """ 368 | ``` 369 | 370 | - Prompt template for computer: 371 | ```python 372 | ## Below is the prompt for computer 373 | prompt = r"""You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 374 | 375 | ## Output Format 376 | ```\nThought: ... 377 | Action: ...\n``` 378 | 379 | ## Action Space 380 | 381 | click(start_box='<|box_start|>(x1,y1)<|box_end|>') 382 | left_double(start_box='<|box_start|>(x1,y1)<|box_end|>') 383 | right_single(start_box='<|box_start|>(x1,y1)<|box_end|>') 384 | drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') 385 | hotkey(key='') 386 | type(content='') #If you want to submit your input, use \"\ 387 | \" at the end of `content`. 388 | scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left') 389 | wait() #Sleep for 5s and take a screenshot to check for any changes. 390 | finished() 391 | call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help. 392 | 393 | 394 | ## Note 395 | - Use Chinese in `Thought` part. 396 | - Summarize your next action (with its target element) in one sentence in `Thought` part. 397 | 398 | ## User Instruction 399 | """ 400 | ``` 401 | 402 | ### Local Deployment [Ollama] 403 | Ollama will be coming soon. Please be patient and wait~ 😊 404 | 449 | 450 | ### Explanation of Inference Results 451 | 452 | #### Coordinate Mapping 453 | The model generates a 2D coordinate output that represents relative positions. To convert these values to image-relative coordinates, divide each component by 1000 to obtain values in the range [0,1]. The absolute coordinates required by the Action can be calculated by: 454 | - X absolute = X relative × image width 455 | - Y absolute = Y relative × image height 456 | 457 | For example, given a screen size: 1920 × 1080, and the model generates a coordinate output of (235, 512). The X absolute is `round(1920*235/1000)=451`. The Y absolute is `round(1080*512/1000)=553`. The absolute coordinate is (451, 553) 458 | 459 | ## Use in desktop and web automation 460 | 461 | To experience UI-TARS agent in desktop, you may refer to [UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop). We recommend using the **7B/72B DPO model** on desktop. 462 | 463 | [Midscene.js](https://github.com/web-infra-dev/Midscene) is an open-source web automation SDK that has supported UI-TARS model. Developers can use javascript and natural language to control the browser. See [this guide](https://midscenejs.com/choose-a-model) for more details about setting up the model. 464 | 465 | ## License 466 | 467 | UI-TARS is licensed under the Apache License 2.0. 468 | 469 | ## Acknowledgements 470 | This project builds upon and extends the capabilities of Qwen2-VL, a powerful vision-language model, which serves as the foundational architecture for UI-TARS. We would like to acknowledge the contributions of the developers and researchers behind Qwen2-VL for their groundbreaking work in the field of multimodal AI and for providing a robust base for further advancements. 471 | 472 | Additionally, we thank the broader open-source community for their datasets, tools, and insights that have facilitated the development of UI-TARS. These collaborative efforts continue to push the boundaries of what GUI automation and AI-driven agents can achieve. 473 | 474 | ## Citation 475 | If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: 476 | 477 | ```BibTeX 478 | @article{qin2025ui, 479 | title={UI-TARS: Pioneering Automated GUI Interaction with Native Agents}, 480 | author={Qin, Yujia and Ye, Yining and Fang, Junjie and Wang, Haoming and Liang, Shihao and Tian, Shizuo and Zhang, Junda and Li, Jiahao and Li, Yunxin and Huang, Shijue and others}, 481 | journal={arXiv preprint arXiv:2501.12326}, 482 | year={2025} 483 | } 484 | ``` 485 | -------------------------------------------------------------------------------- /UI_TARS_paper.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytedance/UI-TARS/a67d3d8cae28923d0159f83d400d310c90815b71/UI_TARS_paper.pdf -------------------------------------------------------------------------------- /UI_TARS_paper_preview.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytedance/UI-TARS/a67d3d8cae28923d0159f83d400d310c90815b71/UI_TARS_paper_preview.pdf -------------------------------------------------------------------------------- /codes/.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | .pytest_cache/ 4 | *.py[oc] 5 | build/ 6 | dist/ 7 | wheels/ 8 | *.egg-info 9 | 10 | # Virtual environments 11 | .venv 12 | .DS_Store 13 | -------------------------------------------------------------------------------- /codes/.python-version: -------------------------------------------------------------------------------- 1 | 3.10 2 | -------------------------------------------------------------------------------- /codes/README.md: -------------------------------------------------------------------------------- 1 | # ui-tars 2 | 3 | A python package for parsing VLM-generated GUI action instructions into executable pyautogui codes. 4 | 5 | --- 6 | 7 | ## Introduction 8 | 9 | `ui-tars` is a Python package for parsing VLM-generated GUI action instructions, automatically generating pyautogui scripts, and supporting coordinate conversion and smart image resizing. 10 | 11 | - Supports multiple VLM output formats (e.g., Qwen-VL, Seed-VL) 12 | - Automatically handles coordinate scaling and format conversion 13 | - One-click generation of pyautogui automation scripts 14 | 15 | --- 16 | 17 | ## Quick Start 18 | 19 | ### Installation 20 | 21 | ```bash 22 | pip install ui-tars 23 | # or 24 | uv pip install ui-tars 25 | ``` 26 | 27 | ### Parse output into structured actions 28 | 29 | ```python 30 | from ui_tars.action_parser import parse_action_to_structure_output, parsing_response_to_pyautogui_code 31 | 32 | response = "Thought: Click the button\nAction: click(point='200 300')" 33 | original_image_width, original_image_height = 1920, 1080 34 | parsed_dict = parse_action_to_structure_output( 35 | response, 36 | factor=1000, 37 | origin_resized_height=original_image_height, 38 | origin_resized_width=original_image_width, 39 | model_type="doubao" 40 | ) 41 | print(parsed_dict) 42 | parsed_pyautogui_code = parsing_response_to_pyautogui_code( 43 | responses=parsed_dict, 44 | image_height=original_image_height, 45 | image_width=original_image_width 46 | ) 47 | print(parsed_pyautogui_code) 48 | ``` 49 | 50 | ### Generate pyautogui automation script 51 | 52 | ```python 53 | from ui_tars.action_parser import parsing_response_to_pyautogui_code 54 | 55 | pyautogui_code = parsing_response_to_pyautogui_code(parsed_dict, original_image_height, original_image_width) 56 | print(pyautogui_code) 57 | ``` 58 | 59 | ### Visualize coordinates on the image (optional) 60 | 61 | ```python 62 | from PIL import Image, ImageDraw 63 | import numpy as np 64 | import matplotlib.pyplot as plt 65 | 66 | image = Image.open("your_image_path.png") 67 | start_box = parsed_dict[0]["action_inputs"]["start_box"] 68 | coordinates = eval(start_box) 69 | x1 = int(coordinates[0] * original_image_width) 70 | y1 = int(coordinates[1] * original_image_height) 71 | draw = ImageDraw.Draw(image) 72 | radius = 5 73 | draw.ellipse((x1 - radius, y1 - radius, x1 + radius, y1 + radius), fill="red", outline="red") 74 | plt.imshow(np.array(image)) 75 | plt.axis("off") 76 | plt.show() 77 | ``` 78 | 79 | --- 80 | 81 | ## API Documentation 82 | 83 | ### parse_action_to_structure_output 84 | 85 | ```python 86 | def parse_action_to_structure_output( 87 | text: str, 88 | factor: int, 89 | origin_resized_height: int, 90 | origin_resized_width: int, 91 | model_type: str = "qwen25vl", 92 | max_pixels: int = 16384 * 28 * 28, 93 | min_pixels: int = 100 * 28 * 28 94 | ) -> list[dict]: 95 | ... 96 | ``` 97 | 98 | **Description:** 99 | Parses output action instructions into structured dictionaries, automatically handling coordinate scaling and box/point format conversion. 100 | 101 | **Parameters:** 102 | - `text`: The output string 103 | - `factor`: Scaling factor 104 | - `origin_resized_height`/`origin_resized_width`: Original image height/width 105 | - `model_type`: Model type (e.g., "qwen25vl", "doubao") 106 | - `max_pixels`/`min_pixels`: Image pixel upper/lower limits 107 | 108 | **Returns:** 109 | A list of structured actions, each as a dict with fields like `action_type`, `action_inputs`, `thought`, etc. 110 | 111 | --- 112 | 113 | ### parsing_response_to_pyautogui_code 114 | 115 | ```python 116 | def parsing_response_to_pyautogui_code( 117 | responses: dict | list[dict], 118 | image_height: int, 119 | image_width: int, 120 | input_swap: bool = True 121 | ) -> str: 122 | ... 123 | ``` 124 | 125 | **Description:** 126 | Converts structured actions into a pyautogui script string, supporting click, type, hotkey, drag, scroll, and more. 127 | 128 | **Parameters:** 129 | - `responses`: Structured actions (dict or list of dicts) 130 | - `image_height`/`image_width`: Image height/width 131 | - `input_swap`: Whether to use clipboard paste for typing (default True) 132 | 133 | **Returns:** 134 | A pyautogui script string, ready for automation execution. 135 | 136 | --- 137 | 138 | ## Contribution 139 | 140 | Contributions, issues, and suggestions are welcome! 141 | 142 | --- 143 | 144 | ## License 145 | 146 | Apache-2.0 License 147 | -------------------------------------------------------------------------------- /codes/makefile: -------------------------------------------------------------------------------- 1 | test: 2 | uv run python3 -m unittest discover tests '*_test.py' 3 | -------------------------------------------------------------------------------- /codes/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "ui-tars" 3 | version = "0.1.4" 4 | description = "Parsing LLM-generated GUI action instructions, automatically generating pyautogui scripts, and supporting coordinate conversion and smart image resizing." 5 | readme = "README.md" 6 | authors = [ 7 | { name = "liangshihao.0828", email = "liangshihao.0828@bytedance.com" }, 8 | { name = "jinxin001", email = "jinxin001@bytedance.com" } 9 | ] 10 | requires-python = ">=3.10,<4.0" 11 | dependencies = [] 12 | 13 | [build-system] 14 | requires = ["hatchling"] 15 | build-backend = "hatchling.build" 16 | 17 | [tool.hatch.envs.test.scripts] 18 | test = "python -m unittest discover tests '*_test.py'" 19 | publish = "python -m unittest discover tests '*_test.py' && uv build && uv publish" 20 | 21 | [tool.black] 22 | line-length = 88 23 | target-version = ['py310'] 24 | include = '\.pyi?$' 25 | 26 | [tool.hatch.build] 27 | include = [ 28 | "ui_tars/**/*.py", 29 | "!ui_tars/**/tests/*.py", 30 | "!ui_tars/**/tests.py" 31 | ] 32 | 33 | [tool.uv] 34 | dev-dependencies = [ 35 | "matplotlib>=3.10.3", 36 | "pillow>=11.2.1", 37 | ] 38 | -------------------------------------------------------------------------------- /codes/tests/action_parser_test.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | import os 4 | import sys 5 | 6 | sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) 7 | 8 | from ui_tars.action_parser import ( 9 | parsing_response_to_pyautogui_code, 10 | parse_action, 11 | parse_action_to_structure_output, 12 | ) 13 | 14 | 15 | class TestActionParser(unittest.TestCase): 16 | def test_parse_action(self): 17 | action_str = "click(point='200 300')" 18 | result = parse_action(action_str) 19 | self.assertEqual(result['function'], 'click') 20 | self.assertEqual(result['args']['point'], '200 300') 21 | 22 | def test_parse_action_to_structure_output(self): 23 | text = "Thought: test\nAction: click(point='200 300')" 24 | actions = parse_action_to_structure_output( 25 | text, factor=1000, origin_resized_height=224, origin_resized_width=224 26 | ) 27 | self.assertEqual(actions[0]['action_type'], 'click') 28 | self.assertIn('start_box', actions[0]['action_inputs']) 29 | 30 | def test_parsing_response_to_pyautogui_code(self): 31 | responses = {"action_type": "hotkey", "action_inputs": {"hotkey": "ctrl v"}} 32 | code = parsing_response_to_pyautogui_code(responses, 224, 224) 33 | self.assertIn('pyautogui.hotkey', code) 34 | 35 | 36 | if __name__ == '__main__': 37 | unittest.main() 38 | -------------------------------------------------------------------------------- /codes/tests/inference_test.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) 5 | 6 | 7 | from PIL import Image 8 | import matplotlib.pyplot as plt 9 | 10 | import json 11 | import base64 12 | from io import BytesIO 13 | from PIL import Image 14 | 15 | import math 16 | 17 | from ui_tars.action_parser import IMAGE_FACTOR, MIN_PIXELS, MAX_PIXELS, MAX_RATIO 18 | 19 | 20 | def round_by_factor(number: int, factor: int) -> int: 21 | """Returns the closest integer to 'number' that is divisible by 'factor'.""" 22 | return round(number / factor) * factor 23 | 24 | 25 | def ceil_by_factor(number: int, factor: int) -> int: 26 | """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" 27 | return math.ceil(number / factor) * factor 28 | 29 | 30 | def floor_by_factor(number: int, factor: int) -> int: 31 | """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" 32 | return math.floor(number / factor) * factor 33 | 34 | 35 | def smart_resize( 36 | height: int, 37 | width: int, 38 | factor: int = IMAGE_FACTOR, 39 | min_pixels: int = MIN_PIXELS, 40 | max_pixels: int = MAX_PIXELS, 41 | ) -> tuple[int, int]: 42 | """ 43 | Rescales the image so that the following conditions are met: 44 | 45 | 1. Both dimensions (height and width) are divisible by 'factor'. 46 | 47 | 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 48 | 49 | 3. The aspect ratio of the image is maintained as closely as possible. 50 | """ 51 | if max(height, width) / min(height, width) > MAX_RATIO: 52 | raise ValueError( 53 | f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" 54 | ) 55 | h_bar = max(factor, round_by_factor(height, factor)) 56 | w_bar = max(factor, round_by_factor(width, factor)) 57 | if h_bar * w_bar > max_pixels: 58 | beta = math.sqrt((height * width) / max_pixels) 59 | h_bar = floor_by_factor(height / beta, factor) 60 | w_bar = floor_by_factor(width / beta, factor) 61 | elif h_bar * w_bar < min_pixels: 62 | beta = math.sqrt(min_pixels / (height * width)) 63 | h_bar = ceil_by_factor(height * beta, factor) 64 | w_bar = ceil_by_factor(width * beta, factor) 65 | return h_bar, w_bar 66 | 67 | 68 | if __name__ == '__main__': 69 | 70 | # Assume model output 71 | model_raw_response = """Thought: xxx 72 | Action: click(start_box='(197,525)')""" 73 | 74 | # Please use re to parse the coordinate values 75 | model_output_width = 197 76 | model_output_height = 525 77 | 78 | # Open the image 79 | img = Image.open('./data/coordinate_process_image.png') 80 | width, height = img.size 81 | print(f'Original coordinate: {width}, {height}') 82 | 83 | # Calculate the new dimensions 84 | new_height, new_width = smart_resize(height, width) 85 | new_coordinate = ( 86 | int(model_output_width / new_width * width), 87 | int(model_output_height / new_height * height), 88 | ) 89 | print(f'Resized dimensions: {new_width}, {new_height}') 90 | print(new_coordinate) 91 | 92 | # Display the image 93 | plt.imshow(img) 94 | plt.scatter( 95 | [new_coordinate[0]], [new_coordinate[1]], c='red', s=50 96 | ) # Mark the point with a red dot 97 | plt.title('Visualize Coordinate') 98 | plt.axis('off') # Set to 'off' to hide the axes 99 | plt.savefig('./data/coordinate_process_image_som.png', dpi=350) 100 | -------------------------------------------------------------------------------- /codes/ui_tars/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytedance/UI-TARS/a67d3d8cae28923d0159f83d400d310c90815b71/codes/ui_tars/__init__.py -------------------------------------------------------------------------------- /codes/ui_tars/action_parser.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates 2 | # SPDX-License-Identifier: Apache-2.0 3 | import re 4 | import ast 5 | import math 6 | 7 | IMAGE_FACTOR = 28 8 | MIN_PIXELS = 100 * 28 * 28 9 | MAX_PIXELS = 16384 * 28 * 28 10 | MAX_RATIO = 200 11 | 12 | 13 | def convert_point_to_coordinates(text, is_answer=False): 14 | # 匹配 后面的四个数字 15 | pattern = r"(\d+)\s+(\d+)" 16 | 17 | def replace_match(match): 18 | x1, y1 = map(int, match.groups()) 19 | x = (x1 + x1) // 2 # 使用截断取整 20 | y = (y1 + y1) // 2 # 使用截断取整 21 | if is_answer: 22 | return f"({x},{y})" # 只返回 (x, y) 格式 23 | return f"({x},{y})" # 返回带标签的格式 24 | 25 | # 去掉 [EOS] 并替换 坐标 26 | text = re.sub(r"\[EOS\]", "", text) 27 | return re.sub(pattern, replace_match, text).strip() 28 | 29 | 30 | # 定义一个函数来解析每个 action 31 | def parse_action(action_str): 32 | try: 33 | # 解析字符串为 AST 节点 34 | node = ast.parse(action_str, mode='eval') 35 | 36 | # 确保节点是一个表达式 37 | if not isinstance(node, ast.Expression): 38 | raise ValueError("Not an expression") 39 | 40 | # 获取表达式的主体 41 | call = node.body 42 | 43 | # 确保主体是一个函数调用 44 | if not isinstance(call, ast.Call): 45 | raise ValueError("Not a function call") 46 | 47 | # 获取函数名 48 | if isinstance(call.func, ast.Name): 49 | func_name = call.func.id 50 | elif isinstance(call.func, ast.Attribute): 51 | func_name = call.func.attr 52 | else: 53 | func_name = None 54 | 55 | # 获取关键字参数 56 | kwargs = {} 57 | for kw in call.keywords: 58 | key = kw.arg 59 | # 处理不同类型的值，这里假设都是常量 60 | if isinstance(kw.value, ast.Constant): 61 | value = kw.value.value 62 | elif isinstance(kw.value, ast.Str): # 兼容旧版本 Python 63 | value = kw.value.s 64 | else: 65 | value = None 66 | kwargs[key] = value 67 | 68 | return {'function': func_name, 'args': kwargs} 69 | 70 | except Exception as e: 71 | print(f"Failed to parse action '{action_str}': {e}") 72 | return None 73 | 74 | 75 | def escape_single_quotes(text): 76 | # 匹配未转义的单引号（不匹配 \\'） 77 | pattern = r"(? int: 82 | """Returns the closest integer to 'number' that is divisible by 'factor'.""" 83 | return round(number / factor) * factor 84 | 85 | 86 | def ceil_by_factor(number: int, factor: int) -> int: 87 | """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'.""" 88 | return math.ceil(number / factor) * factor 89 | 90 | 91 | def floor_by_factor(number: int, factor: int) -> int: 92 | """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'.""" 93 | return math.floor(number / factor) * factor 94 | 95 | 96 | def linear_resize(height: int, 97 | width: int, 98 | factor: int = IMAGE_FACTOR, 99 | min_pixels: int = MIN_PIXELS, 100 | max_pixels: int = MAX_PIXELS) -> tuple[int, int]: 101 | if width * height > max_pixels: 102 | """ 103 | 如果图片超过/低于像素限制，则计算一个缩放因子resize_factor，使图片的像素数缩小到等于或小于max_pixels。这个缩放因子是通过开平方根计算的，确保纵横比保持不变,这样原始的相对坐标可以不经转换直接复用 104 | """ 105 | resize_factor = math.sqrt(max_pixels / (width * height)) 106 | width, height = int(width * resize_factor), int(height * resize_factor) 107 | if width * height < min_pixels: 108 | resize_factor = math.sqrt(min_pixels / (width * height)) 109 | width, height = math.ceil(width * resize_factor), math.ceil( 110 | height * resize_factor) 111 | 112 | return height, width 113 | 114 | 115 | def smart_resize(height: int, 116 | width: int, 117 | factor: int = IMAGE_FACTOR, 118 | min_pixels: int = MIN_PIXELS, 119 | max_pixels: int = MAX_PIXELS) -> tuple[int, int]: 120 | """ 121 | Rescales the image so that the following conditions are met: 122 | 123 | 1. Both dimensions (height and width) are divisible by 'factor'. 124 | 125 | 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 126 | 127 | 3. The aspect ratio of the image is maintained as closely as possible. 128 | """ 129 | if max(height, width) / min(height, width) > MAX_RATIO: 130 | raise ValueError( 131 | f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}" 132 | ) 133 | h_bar = max(factor, round_by_factor(height, factor)) 134 | w_bar = max(factor, round_by_factor(width, factor)) 135 | if h_bar * w_bar > max_pixels: 136 | beta = math.sqrt((height * width) / max_pixels) 137 | h_bar = floor_by_factor(height / beta, factor) 138 | w_bar = floor_by_factor(width / beta, factor) 139 | elif h_bar * w_bar < min_pixels: 140 | beta = math.sqrt(min_pixels / (height * width)) 141 | h_bar = ceil_by_factor(height * beta, factor) 142 | w_bar = ceil_by_factor(width * beta, factor) 143 | return h_bar, w_bar 144 | 145 | 146 | def parse_action_to_structure_output(text, 147 | factor, 148 | origin_resized_height, 149 | origin_resized_width, 150 | model_type="qwen25vl", 151 | max_pixels=16384 * 28 * 28, 152 | min_pixels=100 * 28 * 28): 153 | text = text.strip() 154 | 155 | if "" in text: 156 | text = convert_point_to_coordinates(text) 157 | if "start_point=" in text: 158 | text = text.replace("start_point=", "start_box=") 159 | if "end_point=" in text: 160 | text = text.replace("end_point=", "end_box=") 161 | if "point=" in text: 162 | text = text.replace("point=", "start_box=") 163 | 164 | if model_type == "qwen25vl": 165 | smart_resize_height, smart_resize_width = smart_resize( 166 | origin_resized_height, 167 | origin_resized_width, 168 | factor=IMAGE_FACTOR, 169 | min_pixels=min_pixels, 170 | max_pixels=max_pixels) 171 | 172 | # 正则表达式匹配 Action 字符串 173 | if text.startswith("Thought:"): 174 | thought_pattern = r"Thought: (.+?)(?=\s*Action: |$)" 175 | thought_hint = "Thought: " 176 | elif text.startswith("Reflection:"): 177 | thought_pattern = r"Reflection: (.+?)Action_Summary: (.+?)(?=\s*Action: |$)" 178 | thought_hint = "Reflection: " 179 | elif text.startswith("Action_Summary:"): 180 | thought_pattern = r"Action_Summary: (.+?)(?=\s*Action: |$)" 181 | thought_hint = "Action_Summary: " 182 | else: 183 | thought_pattern = r"Thought: (.+?)(?=\s*Action: |$)" 184 | thought_hint = "Thought: " 185 | reflection, thought = None, None 186 | thought_match = re.search(thought_pattern, text, re.DOTALL) 187 | if thought_match: 188 | if len(thought_match.groups()) == 1: 189 | thought = thought_match.group(1).strip() 190 | elif len(thought_match.groups()) == 2: 191 | thought = thought_match.group(2).strip() 192 | reflection = thought_match.group(1).strip() 193 | assert "Action:" in text 194 | action_str = text.split("Action: ")[-1] 195 | 196 | tmp_all_action = action_str.split(")\n\n") 197 | all_action = [] 198 | for action_str in tmp_all_action: 199 | if "type(content" in action_str: 200 | if not action_str.strip().endswith(")"): 201 | action_str = action_str.strip() + ")" 202 | # 正则表达式匹配 content 中的字符串并转义单引号 203 | def escape_quotes(match): 204 | content = match.group(1) # 获取 content 的值 205 | return content 206 | 207 | # 使用正则表达式进行替换 208 | pattern = r"type\(content='(.*?)'\)" # 匹配 type(content='...') 209 | if re.search(pattern, action_str): # 检查是否有匹配项 210 | content = re.sub(pattern, escape_quotes, action_str) 211 | else: 212 | raise ValueError("Pattern not found in the input string.") 213 | 214 | # 处理字符串 215 | action_str = escape_single_quotes(content) 216 | action_str = "type(content='" + action_str + "')" 217 | if not action_str.strip().endswith(")"): 218 | action_str = action_str.strip() + ")" 219 | all_action.append(action_str) 220 | 221 | parsed_actions = [ 222 | parse_action(action.replace("\n", "\\n").lstrip()) 223 | for action in all_action 224 | ] 225 | actions = [] 226 | for action_instance, raw_str in zip(parsed_actions, all_action): 227 | if action_instance == None: 228 | print(f"Action can't parse: {raw_str}") 229 | raise ValueError(f"Action can't parse: {raw_str}") 230 | action_type = action_instance["function"] 231 | params = action_instance["args"] 232 | 233 | # import pdb; pdb.set_trace() 234 | action_inputs = {} 235 | for param_name, param in params.items(): 236 | if param == "": continue 237 | param = param.lstrip() # 去掉引号和多余的空格 238 | # 处理start_box或者end_box参数格式 'x1 y1 x2 y2' 239 | action_inputs[param_name.strip()] = param 240 | 241 | if "start_box" in param_name or "end_box" in param_name: 242 | ori_box = param 243 | # Remove parentheses and split the string by commas 244 | numbers = ori_box.replace("(", "").replace(")", "").split(",") 245 | 246 | # Convert to float and scale by 1000 247 | # Qwen2.5vl output absolute coordinates, qwen2vl output relative coordinates 248 | if model_type == "qwen25vl": 249 | float_numbers = [] 250 | for num_idx, num in enumerate(numbers): 251 | num = float(num) 252 | if (num_idx + 1) % 2 == 0: 253 | float_numbers.append( 254 | float(num / smart_resize_height)) 255 | else: 256 | float_numbers.append( 257 | float(num / smart_resize_width)) 258 | else: 259 | float_numbers = [float(num) / factor for num in numbers] 260 | 261 | if len(float_numbers) == 2: 262 | float_numbers = [ 263 | float_numbers[0], float_numbers[1], float_numbers[0], 264 | float_numbers[1] 265 | ] 266 | action_inputs[param_name.strip()] = str(float_numbers) 267 | 268 | # import pdb; pdb.set_trace() 269 | actions.append({ 270 | "reflection": reflection, 271 | "thought": thought, 272 | "action_type": action_type, 273 | "action_inputs": action_inputs, 274 | "text": text 275 | }) 276 | return actions 277 | 278 | 279 | def parsing_response_to_pyautogui_code(responses, 280 | image_height: int, 281 | image_width: int, 282 | input_swap: bool = True) -> str: 283 | ''' 284 | 将M模型的输出解析为OSWorld中的action，生成pyautogui代码字符串 285 | 参数: 286 | response: 包含模型输出的字典，结构类似于： 287 | { 288 | "action_type": "hotkey", 289 | "action_inputs": { 290 | "hotkey": "v ctrl", 291 | "start_box": None, 292 | "end_box": None 293 | } 294 | } 295 | 返回: 296 | 生成的pyautogui代码字符串 297 | ''' 298 | 299 | pyautogui_code = f"import pyautogui\nimport time\n" 300 | if isinstance(responses, dict): 301 | responses = [responses] 302 | for response_id, response in enumerate(responses): 303 | if "observation" in response: 304 | observation = response["observation"] 305 | else: 306 | observation = "" 307 | 308 | if "thought" in response: 309 | thought = response["thought"] 310 | else: 311 | thought = "" 312 | 313 | if response_id == 0: 314 | pyautogui_code += f"'''\nObservation:\n{observation}\n\nThought:\n{thought}\n'''\n" 315 | else: 316 | pyautogui_code += f"\ntime.sleep(1)\n" 317 | 318 | action_dict = response 319 | action_type = action_dict.get("action_type") 320 | action_inputs = action_dict.get("action_inputs", {}) 321 | 322 | if action_type == "hotkey": 323 | # Parsing hotkey action 324 | if "key" in action_inputs: 325 | hotkey = action_inputs.get("key", "") 326 | else: 327 | hotkey = action_inputs.get("hotkey", "") 328 | 329 | if hotkey == "arrowleft": 330 | hotkey = "left" 331 | 332 | elif hotkey == "arrowright": 333 | hotkey = "right" 334 | 335 | elif hotkey == "arrowup": 336 | hotkey = "up" 337 | 338 | elif hotkey == "arrowdown": 339 | hotkey = "down" 340 | 341 | if hotkey: 342 | # Handle other hotkeys 343 | keys = hotkey.split() # Split the keys by space 344 | convert_keys = [] 345 | for key in keys: 346 | if key == "space": 347 | key = ' ' 348 | convert_keys.append(key) 349 | pyautogui_code += f"\npyautogui.hotkey({', '.join([repr(k) for k in convert_keys])})" 350 | 351 | elif action_type in ["press", "keydown"]: 352 | # Parsing press action 353 | if "key" in action_inputs: 354 | key_to_press = action_inputs.get("key", "") 355 | else: 356 | key_to_press = action_inputs.get("press", "") 357 | 358 | if key_to_press == "arrowleft": 359 | key_to_press = "left" 360 | 361 | elif key_to_press == "arrowright": 362 | key_to_press = "right" 363 | 364 | elif key_to_press == "arrowup": 365 | key_to_press = "up" 366 | 367 | elif key_to_press == "arrowdown": 368 | key_to_press = "down" 369 | 370 | elif key_to_press == "space": 371 | key_to_press = " " 372 | 373 | if key_to_press: 374 | # Simulate pressing a single key 375 | pyautogui_code += f"\npyautogui.keyDown({repr(key_to_press)})" 376 | 377 | elif action_type in ["release", "keyup"]: 378 | # Parsing press action 379 | if "key" in action_inputs: 380 | key_to_press = action_inputs.get("key", "") 381 | else: 382 | key_to_press = action_inputs.get("press", "") 383 | 384 | if key_to_press == "arrowleft": 385 | key_to_press = "left" 386 | 387 | elif key_to_press == "arrowright": 388 | key_to_press = "right" 389 | 390 | elif key_to_press == "arrowup": 391 | key_to_press = "up" 392 | 393 | elif key_to_press == "arrowdown": 394 | key_to_press = "down" 395 | 396 | elif key_to_press == "space": 397 | key_to_press = " " 398 | 399 | if key_to_press: 400 | # Simulate pressing a single key 401 | pyautogui_code += f"\npyautogui.keyUp({repr(key_to_press)})" 402 | 403 | elif action_type == "type": 404 | # Parsing typing action using clipboard 405 | content = action_inputs.get("content", "") 406 | content = escape_single_quotes(content) 407 | stripped_content = content 408 | if content.endswith("\n") or content.endswith("\\n"): 409 | stripped_content = stripped_content.rstrip("\\n").rstrip("\n") 410 | if content: 411 | if input_swap: 412 | pyautogui_code += f"\nimport pyperclip" 413 | pyautogui_code += f"\npyperclip.copy('{stripped_content}')" 414 | pyautogui_code += f"\npyautogui.hotkey('ctrl', 'v')" 415 | pyautogui_code += f"\ntime.sleep(0.5)\n" 416 | if content.endswith("\n") or content.endswith("\\n"): 417 | pyautogui_code += f"\npyautogui.press('enter')" 418 | else: 419 | pyautogui_code += f"\npyautogui.write('{stripped_content}', interval=0.1)" 420 | pyautogui_code += f"\ntime.sleep(0.5)\n" 421 | if content.endswith("\n") or content.endswith("\\n"): 422 | pyautogui_code += f"\npyautogui.press('enter')" 423 | 424 | elif action_type in ["drag", "select"]: 425 | # Parsing drag or select action based on start and end_boxes 426 | start_box = action_inputs.get("start_box") 427 | end_box = action_inputs.get("end_box") 428 | if start_box and end_box: 429 | x1, y1, x2, y2 = eval( 430 | start_box) # Assuming box is in [x1, y1, x2, y2] 431 | sx = round(float((x1 + x2) / 2) * image_width, 3) 432 | sy = round(float((y1 + y2) / 2) * image_height, 3) 433 | x1, y1, x2, y2 = eval( 434 | end_box) # Assuming box is in [x1, y1, x2, y2] 435 | ex = round(float((x1 + x2) / 2) * image_width, 3) 436 | ey = round(float((y1 + y2) / 2) * image_height, 3) 437 | pyautogui_code += ( 438 | f"\npyautogui.moveTo({sx}, {sy})\n" 439 | f"\npyautogui.dragTo({ex}, {ey}, duration=1.0)\n") 440 | 441 | elif action_type == "scroll": 442 | # Parsing scroll action 443 | start_box = action_inputs.get("start_box") 444 | if start_box: 445 | x1, y1, x2, y2 = eval( 446 | start_box) # Assuming box is in [x1, y1, x2, y2] 447 | x = round(float((x1 + x2) / 2) * image_width, 3) 448 | y = round(float((y1 + y2) / 2) * image_height, 3) 449 | 450 | # # 先点对应区域，再滚动 451 | # pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')" 452 | else: 453 | x = None 454 | y = None 455 | direction = action_inputs.get("direction", "") 456 | 457 | if x == None: 458 | if "up" in direction.lower(): 459 | pyautogui_code += f"\npyautogui.scroll(5)" 460 | elif "down" in direction.lower(): 461 | pyautogui_code += f"\npyautogui.scroll(-5)" 462 | else: 463 | if "up" in direction.lower(): 464 | pyautogui_code += f"\npyautogui.scroll(5, x={x}, y={y})" 465 | elif "down" in direction.lower(): 466 | pyautogui_code += f"\npyautogui.scroll(-5, x={x}, y={y})" 467 | 468 | elif action_type in [ 469 | "click", "left_single", "left_double", "right_single", "hover" 470 | ]: 471 | # Parsing mouse click actions 472 | start_box = action_inputs.get("start_box") 473 | start_box = str(start_box) 474 | if start_box: 475 | start_box = eval(start_box) 476 | if len(start_box) == 4: 477 | x1, y1, x2, y2 = start_box # Assuming box is in [x1, y1, x2, y2] 478 | elif len(start_box) == 2: 479 | x1, y1 = start_box 480 | x2 = x1 481 | y2 = y1 482 | x = round(float((x1 + x2) / 2) * image_width, 3) 483 | y = round(float((y1 + y2) / 2) * image_height, 3) 484 | if action_type == "left_single" or action_type == "click": 485 | pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')" 486 | elif action_type == "left_double": 487 | pyautogui_code += f"\npyautogui.doubleClick({x}, {y}, button='left')" 488 | elif action_type == "right_single": 489 | pyautogui_code += f"\npyautogui.click({x}, {y}, button='right')" 490 | elif action_type == "hover": 491 | pyautogui_code += f"\npyautogui.moveTo({x}, {y})" 492 | 493 | elif action_type in ["finished"]: 494 | pyautogui_code = f"DONE" 495 | 496 | else: 497 | pyautogui_code += f"\n# Unrecognized action type: {action_type}" 498 | 499 | return pyautogui_code 500 | 501 | 502 | def add_box_token(input_string): 503 | # Step 1: Split the string into individual actions 504 | if "Action: " in input_string and "start_box=" in input_string: 505 | suffix = input_string.split("Action: ")[0] + "Action: " 506 | actions = input_string.split("Action: ")[1:] 507 | processed_actions = [] 508 | for action in actions: 509 | action = action.strip() 510 | # Step 2: Extract coordinates (start_box or end_box) using regex 511 | coordinates = re.findall( 512 | r"(start_box|end_box)='\((\d+),\s*(\d+)\)'", action) 513 | 514 | updated_action = action # Start with the original action 515 | for coord_type, x, y in coordinates: 516 | # Convert x and y to integers 517 | updated_action = updated_action.replace( 518 | f"{coord_type}='({x},{y})'", 519 | f"{coord_type}='<|box_start|>({x},{y})<|box_end|>'") 520 | processed_actions.append(updated_action) 521 | 522 | # Step 5: Reconstruct the final string 523 | final_string = suffix + "\n\n".join(processed_actions) 524 | else: 525 | final_string = input_string 526 | return final_string 527 | -------------------------------------------------------------------------------- /codes/ui_tars/prompt.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates 2 | # SPDX-License-Identifier: Apache-2.0 3 | COMPUTER_USE_DOUBAO = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 4 | 5 | ## Output Format 6 | ``` 7 | Thought: ... 8 | Action: ... 9 | ``` 10 | 11 | ## Action Space 12 | 13 | click(point='x1 y1') 14 | left_double(point='x1 y1') 15 | right_single(point='x1 y1') 16 | drag(start_point='x1 y1', end_point='x2 y2') 17 | hotkey(key='ctrl c') # Split keys with a space and use lowercase. Also, do not use more than 3 keys in one hotkey action. 18 | type(content='xxx') # Use escape characters \\', \\\", and \\n in content part to ensure we can parse the content in normal python string format. If you want to submit your input, use \\n at the end of content. 19 | scroll(point='x1 y1', direction='down or up or right or left') # Show more information on the `direction` side. 20 | wait() #Sleep for 5s and take a screenshot to check for any changes. 21 | finished(content='xxx') # Use escape characters \\', \\", and \\n in content part to ensure we can parse the content in normal python string format. 22 | 23 | 24 | ## Note 25 | - Use {language} in `Thought` part. 26 | - Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part. 27 | 28 | ## User Instruction 29 | {instruction} 30 | """ 31 | 32 | MOBILE_USE_DOUBAO = """You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. 33 | ## Output Format 34 | ``` 35 | Thought: ... 36 | Action: ... 37 | ``` 38 | ## Action Space 39 | 40 | click(point='x1 y1') 41 | long_press(point='x1 y1') 42 | type(content='') #If you want to submit your input, use "\\n" at the end of `content`. 43 | scroll(point='x1 y1', direction='down or up or right or left') 44 | open_app(app_name=\'\') 45 | drag(start_point='x1 y1', end_point='x2 y2') 46 | press_home() 47 | press_back() 48 | finished(content='xxx') # Use escape characters \\', \\", and \\n in content part to ensure we can parse the content in normal python string format. 49 | 50 | 51 | ## Note 52 | - Use {language} in `Thought` part. 53 | - Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part. 54 | 55 | ## User Instruction 56 | {instruction} 57 | """ 58 | 59 | GROUNDING_DOUBAO = """You are a GUI agent. 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You need to perform the next action to complete the task. \n\n## Output Format\n\nAction: ...\n\n\n## Action Space\nclick(point='x1 y1'')\n\n## User Instruction 60 | {instruction}""" 61 | -------------------------------------------------------------------------------- /codes/uv.lock: -------------------------------------------------------------------------------- 1 | version = 1 2 | revision = 2 3 | requires-python = ">=3.10, <4.0" 4 | 5 | [[package]] 6 | name = "contourpy" 7 | version = "1.3.2" 8 | source = { registry = "https://pypi.org/simple" } 9 | dependencies = [ 10 | { name = "numpy" }, 11 | ] 12 | sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" } 13 | wheels = [ 14 | { url = 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">=3.10.3" }, 455 | { name = "pillow", specifier = ">=11.2.1" }, 456 | ] 457 | -------------------------------------------------------------------------------- /data/coordinate_process_image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytedance/UI-TARS/a67d3d8cae28923d0159f83d400d310c90815b71/data/coordinate_process_image.png -------------------------------------------------------------------------------- /data/coordinate_process_image_som.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytedance/UI-TARS/a67d3d8cae28923d0159f83d400d310c90815b71/data/coordinate_process_image_som.png -------------------------------------------------------------------------------- /data/training_example.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "role": "system", 4 | "content": "You are a helpful 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