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# UI-TARS
## 论文
`UI-TARS:Pioneering Automated GUI Interaction with Native Agents`
* https://arxiv.org/pdf/2501.12326
## 模型结构
该模型基于Qwen-2-VL，并在此基础上继续训练，以获得UI-TARS。
![alt text](readme_imgs/arch.png)
## 算法原理
增强的GUI感知（Enhanced GUI Perception）
UI-TARS通过构建大规模GUI截图和元数据集，训练模型能够更精准地理解界面元素（按钮、文本框等）、它们的布局关系和状态变化。它不仅能识别元素，还能进行细粒度的界面描述、状态转移描述和视觉问答，以增强对复杂GUI环境的感知能力。
统一的动作建模（Unified Action Modeling）
论文提出了一个统一动作空间，把不同平台（Web、桌面、移动端）上等价的操作（如点击、输入、拖拽）统一标准化。并通过大规模多步操作轨迹（multi-step action traces）数据进行训练，使模型能够连贯地完成复杂多步任务。
系统2推理（System-2 Reasoning）
UI-TARS引入了显式的“思考（Thoughts）”机制，在每步操作之前生成推理步骤，进行任务分解、长期一致性维护、里程碑识别、试错反思等多种推理模式。这种设计让UI-TARS不仅能快速反应（System-1），还能有意识地规划和调整（System-2），应对动态变化和复杂任务。
迭代式自我改进（Iterative Training with Reflective Online Traces）
UI-TARS使用数百台虚拟机在线自动采集、筛选并反思自己的交互轨迹。通过不断地数据迭代采集、错误分析、修正训练，模型能够持续学习，适应新环境和任务，极大减少了对人工标注的依赖。
![alt text](readme_imgs/alg.png)
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run --shm-size 500g --network=host --name=uitars --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install transformers==4.49.0
pip install opencv-python
### Dockerfile（方法二）
docker build -t <IMAGE_NAME>:<TAG> .
docker run --shm-size 500g --network=host --name=uitars --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install transformers==4.49.0
pip install opencv-python
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
dtk: 25.04
python: 3.10
torch: 2.4.1
deepspeed: 0.14.2
flash-attn: 2.6.1
triton: 3.0.0
```
2、其他非特殊库直接按照requirements.txt安装
```
pip install transformers==4.49.0
pip install opencv-python
```
## 数据集
无
## 训练
无
## 推理
```bash
# 运行前需修改文件内参数
# TARS-1.5 推荐
python inference_15.py
# TARS
python inference.py
```
注意：`参考data/test_messages.json`构建自己的数据集，并使用`utils/generate_new_data.py`处理数据。
## result
![alt text](readme_imgs/result1.png)
### 精度
无
## 应用场景
### 算法类别
`对话问答`
### 热点应用行业
`电商,教育,广媒`
## 预训练权重
下载后的模型放在 `checkpoints` 目录（自行创建）
| Model | url |
|-----------|------------------|
| UI-TARS-1.5-7B 推荐 | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) |
| UI-TARS-2B-SFT | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-2B-SFT) |
| UI-TARS-7B-SFT | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT) |
| UI-TARS-7B-DPO | [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO) |
| UI-TARS-72B-SFT| [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-72B-SFT) |
| UI-TARS-72B-DPO| [huggingface](https://huggingface.co/ByteDance-Seed/UI-TARS-72B-DPO) |
## 源码仓库及问题反馈
* https://developer.sourcefind.cn/codes/modelzoo/ui-tars_pytorch
## 参考资料
* https://github.com/bytedance/UI-TARS
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# Tutorial: Processing Model Coordinate Outputs
**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.
---
## Steps:
1. **Visualize the Model's Output Coordinates**
Use the code provided below to process the model's output and visualize the coordinates on the image.
## Code Example
```python
# Assume model output
model_raw_response = """Thought: xxx
Action: click(start_box='(197,525)')"""
# Please use re to parse the coordinate values
model_output_width = 197
model_output_height = 525
from PIL import Image
import matplotlib.pyplot as plt
import json
import base64
from io import BytesIO
from PIL import Image
import math
IMAGE_FACTOR = 28
MIN_PIXELS = 100 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200
VIDEO_MIN_PIXELS = 128 * 28 * 28
VIDEO_MAX_PIXELS = 768 * 28 * 28
FRAME_FACTOR = 2
FPS = 2.0
FPS_MIN_FRAMES = 4
FPS_MAX_FRAMES = 768
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
# Open the image
img = Image.open('./data/coordinate_process_image.png')
width, height = img.size
print(f'Original coordinate: {width}, {height}')
# Calculate the new dimensions
new_height, new_width = smart_resize(height, width)
new_coordinate = (int(model_output_width/new_width * width), int(model_output_height/new_height * height))
print(f'Resized dimensions: {new_width}, {new_height}')
print(new_coordinate)
# Display the image
plt.imshow(img)
plt.scatter([new_coordinate[0]], [new_coordinate[1]], c='red', s=50) # Mark the point with a red dot
plt.title('Visualize Coordinate')
plt.axis('off') # Set to 'off' to hide the axes
plt.savefig('./data/coordinate_process_image_som.png', dpi=350)
```
2. The output SOM image should look like this:
![Output SOM Image](./data/coordinate_process_image_som.png)
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# UI-TARS 1.5 HuggingFace Endpoint Deployment Guide
## 1. HuggingFace Inference Endpoints Cloud Deployment
We use HuggingFace's Inference Endpoints platform to quickly deploy a cloud-based model.
### Deployment Steps
1. **Access the Deployment Interface**
- Click [Deploy from Hugging Face](https://endpoints.huggingface.co/catalog)
![Deploy from Hugging Face](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_1_formal.png?download=true)
- Select the model `UI-TARS 1.5 7B` and click **Import Model**
![Import Model](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_2_formal.png?download=true)
2. **Configure Settings**
- **Hardware Configuration**
- In the `Hardware Configuration` section, choose a GPU instance. Here are the recommendations for different model sizes:
- For the 7B model, select `GPU L40S 1GPU 48G` (Recommended: Nvidia L4 / Nvidia A100).
![Hardware Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_3_formal.png?download=true)
- **Container Configuration**
- Set the following parameters:
- `Max Number of Tokens (per Query)`: 65536
- `Max Batch Prefill Tokens`: 65536
- `Max Input Length (per Query)`: 65537
![Container Configuration](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_4_formal.png?download=true)
- **Environment Variables**
- Add the following environment variables:
- `CUDA_GRAPHS=0` to avoid deployment failures. For details, refer to [issue 2875](https://github.com/huggingface/text-generation-inference/issues/2875).
- `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).
![Environment Variables](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_5_formal.png?download=true)
- **Create Endpoint**
- Click **Create** to set up the endpoint.
![Create Endpoint](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_6_formal.png?download=true)
- **Enter Setup**
- Once the deployment is finished, you will see the confirmation page and need to enter the settings page.
![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_7_formal.png?download=true)
- **Update URI** -
- 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**.
![Complete](https://huggingface.co/datasets/JjjFangg/Demo_video/resolve/main/deployment_8_formal.png?download=true)
## 2. API Usage Example
### **Python Test Code**
```python
# pip install openai
import io
import re
import json
import base64
from PIL import Image
from io import BytesIO
from openai import OpenAI
def add_box_token(input_string):
# Step 1: Split the string into individual actions
if "Action: " in input_string and "start_box=" in input_string:
suffix = input_string.split("Action: ")[0] + "Action: "
actions = input_string.split("Action: ")[1:]
processed_actions = []
for action in actions:
action = action.strip()
# Step 2: Extract coordinates (start_box or end_box) using regex
coordinates = re.findall(r"(start_box|end_box)='\((\d+),\s*(\d+)\)'", action)
updated_action = action # Start with the original action
for coord_type, x, y in coordinates:
# Convert x and y to integers
updated_action = updated_action.replace(f"{coord_type}='({x},{y})'", f"{coord_type}='<|box_start|>({x},{y})<|box_end|>'")
processed_actions.append(updated_action)
# Step 5: Reconstruct the final string
final_string = suffix + "\n\n".join(processed_actions)
else:
final_string = input_string
return final_string
client = OpenAI(
base_url="https:xxx",
api_key="hf_xxx"
)
result = {}
messages = json.load(open("./data/test_messages.json"))
for message in messages:
if message["role"] == "assistant":
message["content"] = add_box_token(message["content"])
print(message["content"])
chat_completion = client.chat.completions.create(
model="tgi",
messages=messages,
top_p=None,
temperature=0.0,
max_tokens=400,
stream=True,
seed=None,
stop=None,
frequency_penalty=None,
presence_penalty=None
)
response = ""
for message in chat_completion:
response += message.choices[0].delta.content
print(response)
```
### **Expected Output** ###
```python
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。
Action: click(start_box='(177,549)')
```
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