README.md

---
base_model: OpenGVLab/InternVL2-4B
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
---

# OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

<div align="center">

[\[🏠Homepage\]](https://qiushisun.github.io/OS-Genesis-Home/) [\[💻Code\]](https://github.com/OS-Copilot/OS-Genesis) [\[📝Paper\]](https://arxiv.org/abs/2412.19723) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d)[\[🤗Data\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d)  

</div>

## Overview
![os-genesis](https://cdn-uploads.huggingface.co/production/uploads/6064a0eeb1703ddba0d458b9/XvcAh92uvJQglmIu_L_nK.png)

We introduce OS-Genesis, an interaction-driven pipeline that synthesizes high-quality and diverse GUI agent trajectory data without human supervision. By leveraging reverse task synthesis, OS-Genesis enables effective training of GUI agents to achieve superior performance on dynamic benchmarks such as AndroidWorld and WebArena.

## Quick Start
OS-Genesis-4B-AC is a mobile action model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B).

###  OS-Genesis AC Family Models
In the following table, we provide an overview of the OS-Genesis AC Family Models used for evaluating the AndroidControl Benchmark.

|   Model Name    |                           Base Model                                            |                           Training Data                                            |                           HF Link                           |
| :-------------: | :-------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------: |
| OS-Genesis-4B-AC | [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B)            | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-4B-AC)  |
| OS-Genesis-7B-AC | [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-7B-AC)  |
| OS-Genesis-8B-AC | [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B)            | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-8B-AC)  |


### Inference Example
First, install the `transformers` library:

```
pip install transformers
```

For additional dependencies, please refer to the [InternVL2 documentation](https://internvl.readthedocs.io/en/latest/get_started/installation.html).

For evaluating the AndroidControl Benchmark, please refer to the [**evaluation code**](https://github.com/OS-Copilot/OS-Genesis/tree/main/evaluation/android_control).

Inference code example:
```python
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, max_num=12):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
path = 'OS-Copilot/OS-Genesis-4B-AC'
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

# set the max number of tiles in `max_num`
pixel_values = load_image('./web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png', max_num=6).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)

question = "<image>
You are a GUI task expert, I will provide you with a high-level instruction, an action history, a screenshot with its corresponding accessibility tree.
 High-level instruction: {high_level_instruction}
 Action history: {action_history}
 Accessibility tree: {a11y_tree}
  Please generate the low-level thought and action for the next step."
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}
Assistant: {response}')
```


## Citation
If you find this repository helpful, feel free to cite our paper:
```bibtex
@article{sun2024genesis,
  title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis},
  author={Sun, Qiushi and Cheng, Kanzhi and Ding, Zichen and Jin, Chuanyang and Wang, Yian and Xu, Fangzhi and Wu, Zhenyu and Jia, Chengyou and Chen, Liheng and Liu, Zhoumianze and others},
  journal={arXiv preprint arXiv:2412.19723},
  year={2024}
}
```