from transformers import CLIPProcessor, CLIPModel from PIL import Image import torch # 加载CLIP模型 model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")# 加载图片和文本 image = Image.open("example_image.jpg") texts =["A dog playing in the park","A futuristic city with flying cars"]# 编码图片和文本 inputs = processor(text=texts, images=image, return_tensors="pt", padding=True) outputs = model(**inputs)# 计算相似性 logits_per_image = outputs.logits_per_image # 图片与文本的相似性 probs = logits_per_image.softmax(dim=1)# 转化为概率print("Text-Image Similarity:", probs)
三、AIGC市场的技术挑战与解决方案
1. 数据质量问题
数据质量直接影响AIGC模型的生成效果。以下代码展示如何对文本数据进行去噪处理。
代码示例：文本数据清洗
import re defclean_text(text):# 去除特殊字符 text = re.sub(r'[^\w\s]','', text)# 转为小写 text = text.lower()# 去除多余空格 text = re.sub(r'\s+',' ', text)return text.strip()# 示例数据 raw_text =" Hello, WORLD! Welcome to AIGC. " cleaned_text = clean_text(raw_text)print("Cleaned Text:", cleaned_text)
2. 模型偏差问题
模型偏差可能导致生成内容的失真。可以通过多样化训练数据来缓解。
代码示例：构建多样化的文本增强
import random defaugment_text(text): synonyms ={"fast":["quick","speedy"],"AI":["artificial intelligence","machine learning"],"future":["prospect","horizon"]} words = text.split() augmented =[random.choice(synonyms.get(word,[word]))forword in words]return" ".join(augmented)# 示例 text ="AI is shaping the fast future" augmented_text = augment_text(text)print("Augmented Text:", augmented_text)
3. 内容真实性问题
为确保生成内容的真实性，可以使用事实验证工具。
代码示例：利用NLP模型进行事实验证
from transformers import pipeline # 加载模型 fact_checker = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")# 检查事实 text ="The Eiffel Tower is located in Paris." candidate_labels =["true","false"] result = fact_checker(text, candidate_labels)print("Fact Checking Result:", result)
四、AIGC的未来趋势
1. 多模态生成成为主流
未来，多模态生成将实现更深层次的语义关联。
2. 垂直领域的深入
AIGC将应用于医疗、法律等领域。以下是一个法律文书生成的简单示例：
代码示例：生成法律文书
from transformers import pipeline # 加载法律文书生成模型 legal_pipeline = pipeline("text-generation", model="nlp-legal/bart-legal")# 输入法律术语 prompt ="In accordance with the law of contracts, the parties agree to" legal_text = legal_pipeline(prompt, max_length=100, num_return_sequences=1)print("Generated Legal Document:", legal_text[0]['generated_text'])
