app.py

import os
import subprocess
import gradio as gr
from diffusers import StableDiffusionPipeline
import torch
from peft import PeftModel

LORA_DIR = "./lora_girls"

# 1. 如果没训练好的 lora-girls，就先训练
if not os.path.exists(LORA_DIR):
    print("🚀 未检测到 LoRA 权重，开始训练 ...")
    result = subprocess.run(["python", "train_lora.py"], capture_output=True, text=True)
    print(result.stdout)
    if result.returncode != 0:
        print("❌ 训练失败：", result.stderr)
        raise SystemExit("训练出错，停止运行")
    print("✅ 训练完成，LoRA 已生成。")

# 2. 训练完成后，加载基座模型 + LoRA
print("🔄 正在加载模型和 LoRA ...")
pipe = StableDiffusionPipeline.from_pretrained("prompthero/openjourney", torch_dtype=torch.float16).to("cuda")
pipe.unet = PeftModel.from_pretrained(pipe.unet, LORA_DIR)
print("✅ 模型加载完成，可以开始生成图片！")

# 3. 定义 Gradio 界面
def infer(prompt):
    image = pipe(prompt).images[0]
    return image

with gr.Blocks() as demo:
    gr.Markdown("# 🖼️ LoRA 文生图 (Girls)")
    prompt = gr.Textbox(label="输入你的提示词")
    output = gr.Image()
    btn = gr.Button("生成")
    btn.click(fn=infer, inputs=prompt, outputs=output)

demo.launch()