75 lines
1.9 KiB
TypeScript
75 lines
1.9 KiB
TypeScript
// http://localhost:3001/api/recipes/image/generate
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import process from 'node:process'
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import { meat, staple, vegetable } from '~/data/food'
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// internal temp
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// const sdBaseUrl = 'http://30.30.168.63:7860/'
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const sdBaseUrl = process.env.SD_API_BASE_URL
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const payload = {
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// denoising_strength: 0,
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prompt: 'food', // 提示词
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negative_prompt: '', // 反向提示词
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seed: -1, // 种子，随机数
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batch_size: 2, // 每次张数
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n_iter: 1, // 生成批次
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steps: 50, // 生成步数
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cfg_scale: 7, // 关键词相关性
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width: 512, // 宽度
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height: 512, // 高度
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restore_faces: false, // 脸部修复
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tiling: false, // 可平埔
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// override_settings: {
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// sd_model_checkpoint: 'wlop-any.ckpt [7331f3bc87]',
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// }, // 一般用于修改本次的生成图片的stable diffusion 模型，用法需保持一致
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// script_args: [
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// 0,
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// true,
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// true,
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// 'LoRA',
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// 'dingzhenlora_v1(fa7c1732cc95)',
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// 1,
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// 1,
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// ], // 一般用于lora模型或其他插件参数，如示例，我放入了一个lora模型， 1，1为两个权重值，一般只用到前面的权重值1
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sampler_index: 'Euler', // 采样方法
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}
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// with api /sdapi/v1/txt2img
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export interface Txt2ImgResponse {
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images: string[]
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}
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const stuffItems = [
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...vegetable,
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...meat,
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...staple,
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]
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export default defineEventHandler(async (e) => {
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const body = await readBody(e)
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const zhFoods = body.foods as string[]
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const enFoods = zhFoods.map((food) => {
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const item = stuffItems.find(item => item.name === food)
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if (item)
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return item.en
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return ''
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})
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// TODO: 过滤 prompt 只能是食材
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// <lora:TODO:1>,
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payload.prompt = `food focus,transparent background,${enFoods.join(',')}`
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console.log(payload.prompt)
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const data = await $fetch<Txt2ImgResponse>('/sdapi/v1/txt2img', {
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baseURL: sdBaseUrl,
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body: payload,
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method: 'POST',
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})
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console.log(data)
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return data
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})
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