| ## ⭐ Quick Start | |
| 1. Load model | |
| ```python | |
| import ast | |
| import torch | |
| from PIL import Image, ImageDraw | |
| from qwen_vl_utils import process_vision_info | |
| from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor | |
| def draw_point(image_input, point=None, radius=5): | |
| if isinstance(image_input, str): | |
| image = Image.open(BytesIO(requests.get(image_input).content)) if image_input.startswith('http') else Image.open(image_input) | |
| else: | |
| image = image_input | |
| if point: | |
| x, y = point[0] * image.width, point[1] * image.height | |
| ImageDraw.Draw(image).ellipse((x - radius, y - radius, x + radius, y + radius), fill='red') | |
| display(image) | |
| return | |
| model = Qwen2VLForConditionalGeneration.from_pretrained( | |
| "showlab/ShowUI-2B", | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto" | |
| ) | |
| min_pixels = 256*28*28 | |
| max_pixels = 1344*28*28 | |
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) | |
| ``` | |
| 2. Load screenshot and query | |
| ```python | |
| img_url = 'web_dbd7514b-9ca3-40cd-b09a-990f7b955da1.png' | |
| query = "Nahant" | |
| _SYSTEM = "Based on the screenshot of the page, I give a text description and you give its corresponding location. The coordinate represents a clickable location [x, y] for an element, which is a relative coordinate on the screenshot, scaled from 0 to 1." | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": _SYSTEM}, | |
| {"type": "image", "image": img_url, "min_pixels": min_pixels, "max_pixels": max_pixels}, | |
| {"type": "text", "text": query} | |
| ], | |
| } | |
| ] | |
| text = processor.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True, | |
| ) | |
| image_inputs, video_inputs = process_vision_info(messages) | |
| inputs = processor( | |
| text=[text], | |
| images=image_inputs, | |
| videos=video_inputs, | |
| padding=True, | |
| return_tensors="pt", | |
| ) | |
| inputs = inputs.to("cuda") | |
| generated_ids = model.generate(**inputs, max_new_tokens=128) | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) | |
| ] | |
| output_text = processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| )[0] | |
| click_xy = ast.literal_eval(output_text) | |
| # [0.73, 0.21] | |
| draw_point(img_url, click_xy, 10) | |
| ``` | |
| This will visualize the grounding results like (where the red points are [x,y]) | |
|  | |