AI 生成内容（AIGC）已成为科技领域的核心热点，覆盖文本、图像及视频等多个维度。我们将从模型现状入手，结合实战代码分析其应用场景与技术瓶颈。
AIGC 的市场现状与挑战
当前主流生成模型已相当成熟，涵盖了从 GPT 系列的文本生成，到 Stable Diffusion 的图像创作，再到 CLIP 的多模态理解。
以文本生成为例，利用 Hugging Face 的 Transformers 库可以快速调用预训练模型。下面是一个基础实现，注意分词器与模型的加载顺序必须一致：
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
input_text = "The future of AI-generated content is"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text:", generated_text)
在实际微调场景中，我们需要准备自定义数据集。这里展示了如何基于本地文件进行简单的语言模型训练配置：
from transformers import TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments, GPT2LMHeadModel
def load_dataset(file_path, tokenizer, block_size=128):
dataset = TextDataset(
tokenizer=tokenizer,
file_path=file_path,
block_size=block_size
)
return dataset
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
dataset = load_dataset("custom_text_data.txt", tokenizer)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
model = GPT2LMHeadModel.from_pretrained("gpt2")
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=8,
save_steps=10_000,
save_total_limit=,
prediction_loss_only=
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset
)
trainer.train()
