免费部署 CF Worker 无限绘画，支持多模型切换，API 可用。
## Cloudflare Worker 无限免费绘画 API**旧帖无法编辑，最新版本请访问以下链接:**
* [第一弹](https://linux.do/t/topic/185850)
* [第二弹](https://linux.do/t/topic/186692)
* [第三弹](https://linux.do/t/topic/222370)
---
**准备工作:**
1. 注册 Cloudflare 并开通 Worker AI Token
2. 注册 sm.ms 图床 (需自行申请: (https://sm.ms))
3. 复制以下代码部署到 Cloudflare Workers
**更新日志 (10 月 3 日):**
* 新增部分绘画模型
* 新增 `--ntl` (强制关闭翻译), `--tl` (强制翻译) 参数控制提示词优化翻译
* 新增测速模型
* 优化自定义图像大小，可使用参数: `--1:1`, `--1:2`, `--3:2`, `--4:3`, `--16:9`, `--9:16`
代码
//本项目授权api_key，防止被恶意调用（填入到one-api/new-api的渠道密钥中）
const API_KEY = "sk-1234567890";
//https://sm.ms 图床key，可自行申请，为空则返回base64编码后的图片
const SMMS_API_KEY = 'xxxxxxxxx';
//cloudflare账号列表，每次请求都会随机从列表里取一个账号
const CF_ACCOUNT_LIST = [{
account_id: "xxxxxxxxx",
token: "xxxxxxxxx"
}];
//在你输入的prompt中添加 ---ntl可强制禁止提示词翻译、优化功能
//在你输入的prompt中添加 ---tl可强制开启提示词翻译、优化功能
//是否开启提示词翻译、优化功能
const CF_IS_TRANSLATE = true;
//示词翻译、优化模型
const CF_TRANSLATE_MODEL = "@cf/qwen/qwen1.5-14b-chat-awq";
const RATIO_MAP = {
"1:1": "1024x1024",
"1:2": "1024x2048",
"3:2": "1536x1024",
"4:3": "1536x2048",
"16:9": "2048x1152",
"9:16": "1152x2048"
}
//模型映射，设置客户端可用的模型。one-api，new-api在添加渠道时可使用“获取模型列表”功能，一键添加模型
const CUSTOMER_MODEL_MAP = {
"test": {
body: {
model: "test"
}
},
"FLUX.1": {
isImage2Image: false,
body: {
model: "@cf/black-forest-labs/flux-1-schnell",
prompt: "",
width: 1024,
height: 1024,
num_steps: 8
},
RATIO_MAP: RATIO_MAP
},
"dreamshaper-8": {
isImage2Image: false,
body: {
model: "@cf/lykon/dreamshaper-8-lcm",
prompt: "",
width: 1024,
height: 1024,
num_steps: 20
},
RATIO_MAP: RATIO_MAP
},
"stable-diffusion-xl-base": {
isImage2Image: false,
body: {
model: "@cf/stabilityai/stable-diffusion-xl-base-1.0",
prompt: "",
width: 1024,
height: 1024,
num_steps: 20
},
RATIO_MAP: RATIO_MAP
},
"stable-diffusion-xl-lightning": {
isImage2Image: false,
body: {
model: "@cf/bytedance/stable-diffusion-xl-lightning",
prompt: "",
width: 1024,
height: 1024,
num_steps: 20
},
RATIO_MAP: RATIO_MAP
},
"stable-diffusion-v1-5": {
isImage2Image: true,
body: {
model: "@cf/runwayml/stable-diffusion-v1-5-inpainting",
prompt: "",
width: 1024,
height: 1024,
num_steps: 20
},
RATIO_MAP: RATIO_MAP
},
"stable-diffusion-v1-5-img2img": {
isImage2Image: true,
body: {
model: "@cf/runwayml/stable-diffusion-v1-5-img2img",
prompt: "",
width: 1024,
height: 1024,
num_steps: 20
},
RATIO_MAP: RATIO_MAP
}
};
async function handleRequest(request) {
try {
if (request.method === "OPTIONS") {
return getResponse("", 204);
}
const authHeader = request.headers.get("Authorization");
if (!authHeader || !authHeader.startsWith("Bearer ") || authHeader.split(" ") !== API_KEY) {
return getResponse("Unauthorized", 401);
}
if (request.url.endsWith("/v1/models")) {
const arrs = [];
Object.keys(CUSTOMER_MODEL_MAP).map(element => arrs.push({
id: element,
object: "model"
}))
const response = {
data: arrs,
success: true
};
return getResponse(JSON.stringify(response), 200);
}
if (request.method !== "POST") {
return getResponse("Only POST requests are allowed", 405);
}
if (!request.url.endsWith("/v1/chat/completions")) {
return getResponse("Not Found", 404);
}
const data = await request.json();
const messages = data.messages || [];
const modelInfo = CUSTOMER_MODEL_MAP || CUSTOMER_MODEL_MAP["FLUX.1"];
const stream = data.stream || false;
const userMessage = messages.reverse().find((msg) => msg.role === "user")?.content;
if (!userMessage) {
return getResponse(JSON.stringify({
error: "未找到用户消息"
}), 400);
}
if (modelInfo.body.model == "test") {
if (stream) {
return handleStreamResponse(userMessage, "", "", data.model, "");
} else {
return handleNonStreamResponse(userMessage, "", "", data.model, "");
}
}
const is_translate = extractTranslate(userMessage);
const size = extractImageSize(userMessage, modelInfo.RATIO_MAP);
const imageUrl = extractImageUrl(userMessage);
const originalPrompt = cleanPromptString(userMessage);
const translatedPrompt = is_translate ? await getPrompt(originalPrompt) : originalPrompt;
let url;
if (!imageUrl) {
url = await generateImage(translatedPrompt, "", modelInfo, size);
} else {
const base64 = await convertImageToBase64(imageUrl);
url = await generateImage(translatedPrompt, base64, modelInfo, size);
}
if (stream) {
return handleStreamResponse(originalPrompt, translatedPrompt, size, data.model, url);
} else {
return handleNonStreamResponse(originalPrompt, translatedPrompt, size, data.model, url);
}
} catch (error) {
return getResponse(JSON.stringify({
error: `处理请求失败: ${error.message}`
}), 500);
}
}
async function generateImage(translatedPrompt, base64Image, modelInfo, imageSize) {
try {
const jsonBody = modelInfo.body;
jsonBody.prompt = translatedPrompt;
jsonBody.width = parseInt(imageSize.split('x'));
jsonBody.height = parseInt(imageSize.split('x'));
if (modelInfo.isImage2Image && base64Image) {
jsonBody.image = [...new Uint8Array(base64ToArrayBuffer(base64Image))];
jsonBody.mask = [...new Uint8Array(base64ToArrayBuffer(base64Image))];
}
let requestBody={};
for (let key in jsonBody) {
if(key!="model"){
requestBody=jsonBody;
}
}
const response = await postRequest(modelInfo.body.model, requestBody);
let image_blob;
let image_b64
try {
const jsonResponse = await response.clone().json();
if (jsonResponse && jsonResponse.success) {
image_b64 = jsonResponse.result.image;
image_blob = base64ToBlob(image_b64, "image/png");
} else {
return "生成图像失败，" + jsonResponse.errors?.message;
}
} catch (error) {
const arrayBuffer = await response.clone().arrayBuffer();
image_b64 = arrayBufferToBase64(arrayBuffer);
image_blob = new Blob(, {
type: "image/png"
});
}
if (SMMS_API_KEY) {
const imageUrl = await uploadImage(image_blob);
return imageUrl;
} else {
return `data:image/webp;base64,${image_b64}`;
}
} catch (error) {
return "图像生成或转换失败，请检查！" + error.message;
}
}
async function uploadImage(imageBlob) {
try {
//const imageBlob = await response.blob();
const formData = new FormData();
formData.append("smfile", imageBlob, "image.jpg");
const uploadResponse = await fetch("https://sm.ms/api/v2/upload", {
method: 'POST',
headers: {
'Authorization': `${SMMS_API_KEY}`,
},
body: formData,
});
if (!uploadResponse.ok) {
throw new Error("Failed to upload image");
}
const uploadResult = await uploadResponse.json();
const imageUrl = uploadResult.data?.url;
return imageUrl;
} catch (error) {
return "图像上传失败，请检查！" + error.message;
}
}
async function convertImageToArrayBuffer(imageUrl) {
try {
const response = await fetch(imageUrl);
if (!response.ok) {
//throw new Error('Failed to download image');
return null;
}
return [...new Uint8Array(await response.arrayBuffer())]
} catch (error) {
return null;
}
}
async function convertImageToBase64(imageUrl) {
try {
const response = await fetch(imageUrl);
if (!response.ok) {
//throw new Error('Failed to download image');
return "";
}
const arrayBuffer = await response.arrayBuffer();
const base64Image = arrayBufferToBase64(arrayBuffer);
return base64Image;
//return `data:image/webp;base64,${base64Image}`;
} catch (error) {
return "";
}
}
function base64ToBlob(base64, mimeType = '') {
let bstr = atob(base64),
n = bstr.length,
u8arr = new Uint8Array(n);
while (n--) {
u8arr = bstr.charCodeAt(n);
}
return new Blob(, {
type: mimeType
});
}
function base64ToArrayBuffer(base64) {
// 解码 base64
let binaryString = atob(base64);
// 创建 ArrayBuffer
let len = binaryString.length;
let bytes = new Uint8Array(len);
// 将每个字符的 UTF-8 值转换为字节
for (let i = 0; i{
event.respondWith(handleRequest(event.request));
});
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