Stable Diffusion为秋叶大佬的绘世2.2.4
webUI api后台：http://127.0.0.1:7860/docs
数据获取结果，python代码在文章尾部
1girl: 0.9883618950843811, 98% solo: 0.9468605518341064, 94% horns: 0.9203381538391113, 92% braid: 0.7536494731903076, 75% brown_hair: 0.7361204624176025, 73% sensitive: 0.7181869745254517, 71% looking_at_viewer: 0.6558270454406738, 65% long_hair: 0.6555134654045105, 65% portrait: 0.5619801878929138, 56% hair_ornament: 0.5276427268981934, 52% lips: 0.5271897912025452, 52% realistic: 0.47530364990234375, 47% brown_eyes: 0.44382530450820923, 44% fur_trim: 0.44058263301849365, 44% red_hair: 0.4004508852958679, 40% upper_body: 0.39194822311401367, 39% mole: 0.35748565196990967, 35% general: 0.2813188433647156, 28% questionable: 0.004140794277191162, 0% explicit: 0.0005668997764587402, 0% 使用/tagger/v1/interrogate，先使用get方法获取model模组有十多个，然后把json_data提交上去就可以了。记得把图片转码为base64。本文章仅用于测试，请仔细阅读api docs，model和threshold按照需求调整即可
import requests import base64 from collections import OrderedDict from PIL import Image url = 'http://127.0.0.1:7860/tagger/v1/interrogate' image_path = 'D:/code/image/6.jpg' model = 'wd14-convnext' threshold = 0.35 #确认照片为上传照片 image = Image.open(image_path) image.show() # 将图片转换为Base64字符串 with open(image_path, 'rb') as file: image_data = file.read() base64_image = base64.b64encode(image_data).decode('utf-8') # 构建请求体的JSON数据 data = { "image": base64_image, "model": model, "threshold": threshold } # 发送POST请求 response = requests.post(url, json=data) # 检查响应状态码 if response.status_code == 200: json_data = response.json() # 处理返回的JSON数据 caption_dict = json_data['caption'] sorted_items = sorted(caption_dict.items(), key=lambda x: x[1], reverse=True) output = '\n'.join([f'{k}: {v}, {int(v * 100)}%' for k, v in sorted_items]) print(output) else: print('Error:', response.status_code) print('Response body:', response.text) 
