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'use strict'
const dotenv = require('dotenv');
const fs = require('fs').promises;
const HfInference = require('@huggingface/inference').HfInference;
dotenv.config();
const inference = new HfInference(process.env.HF_TOKEN);
const REPO_NAME = "black-forest-labs/FLUX.1-schnell"
const IMAGE_SIZES = {
"square": {
height: 512,
width: 512
},
"portrait-3_4": {
height: 512,
width: 384
},
"portrait-9_16": {
height: 512,
width: 288
},
"landscape-4_3": {
height: 384,
width: 512
},
"landscape-16_9": {
height: 288,
width: 512
}
}
module.exports = async function (fastify, opts) {
fastify.get('/:inputs', async function (request, reply) {
let { inputs } = request.params;
const { format } = request.query;
if (format) {
inputs = inputs + " " + format;
}
const slug = inputs.replace(/[^a-zA-Z0-9-_ ]/g, "").replace(/ /g, "-");
const file = await fs.readFile(process.env.PUBLIC_FILE_UPLOAD_DIR + "/" + slug + ".png")?.catch(() => null)
if (file) {
return reply
.header('Content-Type', 'image/jpeg')
.send(file);
}
const { height, width } = IMAGE_SIZES[format ?? "square"] ?? IMAGE_SIZES["square"];
const hfRequest = await inference.textToImage({
inputs,
model: REPO_NAME,
parameters: {
height,
width
}
})
const buffer = await hfRequest.arrayBuffer();
const array = new Uint8Array(buffer);
const dir = await fs.opendir(process.env.PUBLIC_FILE_UPLOAD_DIR).catch(() => null)
if (!dir) await fs.mkdir(process.env.PUBLIC_FILE_UPLOAD_DIR)
await fs.writeFile(process.env.PUBLIC_FILE_UPLOAD_DIR + "/" + slug + ".png", array)
return reply
.header('Content-Type', 'image/jpeg')
.send(array);
})
}
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