Aria-UI/Aria-UI-base

🖼️ Try Aria-UI! · 📖 Project Page · 📌 Paper · ⭐ GitHub · 📚 Aria-UI Dataset

Key Features of Aria-UI

✨ Versatile Grounding Instruction Understanding:
Aria-UI handles diverse grounding instructions, excelling in interpreting varied formats, ensuring robust adaptability across dynamic scenarios or when paired with diverse planning agents.

📝 Context-aware Grounding:
Aria-UI effectively leverages historical input, whether in pure text or text-image-interleaved formats, to improve grounding accuracy.

⚡ Lightweight and Fast:
Aria-UI is a mixture-of-expert model with 3.9B activated parameters per token. It efficiently encodes GUI input of variable sizes and aspect ratios, with ultra-resolution support.

🎉 Superior Performances:
Aria-UI sets new state-of-the-art results on offline and online agent benchmarks.
🏆 1st place on AndroidWorld with 44.8% task success rate and
🥉 3rd place on OSWorld with 15.2% task success rate (Dec. 2024).

Quick Start

Installation

pip install transformers==4.45.0 accelerate==0.34.1 sentencepiece==0.2.0 torchvision requests torch Pillow
pip install flash-attn --no-build-isolation
# For better inference performance, you can install grouped-gemm, which may take 3-5 minutes to install
pip install grouped_gemm==0.1.6

Inference with vllm (strongly recommended)

First, make sure you install the latest version of vLLM so that it supports Aria-UI

pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/nightly/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl

Here is a code snippet for Aria-UI with vllm.

from PIL import Image, ImageDraw
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
import ast

model_path = "Aria-UI/Aria-UI-base"

def main():
    llm = LLM(
        model=model_path,
        tokenizer_mode="slow",
        dtype="bfloat16",
        trust_remote_code=True,
    )

    tokenizer = AutoTokenizer.from_pretrained(
        model_path, trust_remote_code=True, use_fast=False
    )

    instruction = "Try Aria."

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {
                    "type": "text",
                    "text": "Given a GUI image, what are the relative (0-1000) pixel point coordinates for the element corresponding to the following instruction or description: " + instruction,
                }
            ],
        }
    ]

    message = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

    outputs = llm.generate(
        {
            "prompt_token_ids": message,
            "multi_modal_data": {
                "image": [
                    Image.open("examples/aria.png"),
                ],
                "max_image_size": 980,  # [Optional] The max image patch size, default `980`
                "split_image": True,  # [Optional] whether to split the images, default `True`
            },
        },
        sampling_params=SamplingParams(max_tokens=50, top_k=1, stop=["<|im_end|>"]),
    )

    for o in outputs:
        generated_tokens = o.outputs[0].token_ids
        response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
        print(response)
        coords = ast.literal_eval(response.replace("<|im_end|>", "").replace("```", "").replace(" ", "").strip())
        return coords

if __name__ == "__main__":
    main()

Inference with Transfomrers (not recommended)

You can also use the original transformers API for Aria-UI. For instance:

import argparse
import torch
import os
import json
from tqdm import tqdm
import time
from PIL import Image, ImageDraw
from transformers import AutoModelForCausalLM, AutoProcessor
import ast

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

model_path = "Aria-UI/Aria-UI-base"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

image_file = "./examples/aria.png"
instruction = "Try Aria."
image = Image.open(image_file).convert("RGB")

messages = [
    {
        "role": "user",
        "content": [
            {"text": None, "type": "image"},
            {"text": instruction, "type": "text"},
        ],
    }
]

text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt")
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.inference_mode(), torch.amp.autocast("cuda", dtype=torch.bfloat16):
    output = model.generate(
        **inputs,
        max_new_tokens=50,
        stop_strings=["<|im_end|>"],
        tokenizer=processor.tokenizer,
        # do_sample=True,
        # temperature=0.9,
    )

output_ids = output[0][inputs["input_ids"].shape[1] :]
response = processor.decode(output_ids, skip_special_tokens=True)
print(response)

coords = ast.literal_eval(response.replace("<|im_end|>", "").replace("```", "").replace(" ", "").strip())

Citation

If you find our work helpful, please consider citing.

@article{ariaui,
      title={Aria-UI: Visual Grounding for GUI Instructions}, 
      author={Yuhao Yang and Yue Wang and Dongxu Li and Ziyang Luo and Bei Chen and Chao Huang and Junnan Li},
      year={2024},
      journal={arXiv preprint arXiv:2412.16256},
}