如何快速使用StableDiffusion文生图模型_模型服务灵积(DashScope)-阿里云帮助中心

重要

StableDiffusion文生图模型API目前处于"申请体验"阶段，请点击此处申请。

在您的账号未申请体验或申请未通过时，调用StableDiffusion文生图模型API将返回错误状态码。

StableDiffusion文生图模型

说明

支持的领域 / 任务：aigc

StableDiffusion文生图模型目前针对开源社区的stable-diffusion-v1.5版本和stable-diffusion-xl版本进行了服务化支持。

其中，stable-diffusion-v1.5模型通过clip模型能够将文本的embedding和图片embedding映射到相同空间，从而通过输入文本并结合unet的稳定扩散预测噪声的能力，生成图片。是一款基础的文生图模型，得到了业界广泛使用。而stable-diffusion-xl相比于v1.5做了重大的改进，被认为是当前开源文生图模型的SOTA水准，具体改进之处包括：更大的unet backbone，是之前的3倍；增加了refinement模块用于改善生成图片的质量；更高效的训练技巧等。

快速开始

前提条件

已开通服务并获得api-key：开通DashScope并创建API-KEY。
已安装SDK：安装DashScope SDK。

示例代码

以下示例展示了调用 stable-diffusion-xl 模型API进行文生图的示例代码，如果需要调用 Stable Diffusion v1.5版本只需要用 stable-diffusion-v1.5 替换 stable-diffusion-xl 即可。

说明

需要使用您的api-key替换示例中的 your-dashscope-api-key ，代码才能正常运行。

Python

from http import HTTPStatus
from urllib.parse import urlparse, unquote
from pathlib import PurePosixPath
import requests
import dashscope

model = "stable-diffusion-xl"
prompt = "Eagle flying freely in the blue sky and white clouds"


def simple_call():
    rsp = dashscope.ImageSynthesis.call(model=model,
                                        prompt=prompt,
                                        negative_prompt="garfield",
                                        n=1,
                                        size='1024*1024')
    if rsp.status_code == HTTPStatus.OK:
        print(rsp.output)
        print(rsp.usage)
        # save file to current directory
        for result in rsp.output.results:
            file_name = PurePosixPath(unquote(urlparse(result.url).path)).parts[-1]
            with open('./%s' % file_name, 'wb+') as f:
                f.write(requests.get(result.url).content)
    else:
        print('Failed, status_code: %s, code: %s, message: %s' %
              (rsp.status_code, rsp.code, rsp.message))


if __name__ == '__main__':
    simple_call()

Java

// Copyright (c) Alibaba, Inc. and its affiliates.

import java.io.IOException;
import java.net.URL;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Map;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesis;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisParam;
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;

public class Main {
  private static final OkHttpClient CLIENT = new OkHttpClient();
  private static final String MODEL = "stable-diffusion-xl";
  private static final String PROMPT = "Eagle flying freely in the blue sky and white clouds";
  private static final String SIZE = "1024*1024";

  public static void basicCall() throws ApiException, NoApiKeyException, IOException {
    ImageSynthesis is = new ImageSynthesis();
    ImageSynthesisParam param =
        ImageSynthesisParam.builder()
            .model(Main.MODEL)
            .n(1)
            .size(Main.SIZE)
            .prompt(Main.PROMPT)
            .negativePrompt("garfield")
            .build();

    ImageSynthesisResult result = is.call(param);
    System.out.println(result);
    // save image to local files.
    for(Map<String, String> item :result.getOutput().getResults()){
      String paths = new URL(item.get("url")).getPath();
      String[] parts = paths.split("/");
      String fileName = parts[parts.length-1];
      Request request = new Request.Builder()
        .url(item.get("url"))
        .build();

      try (Response response = CLIENT.newCall(request).execute()) {
        if (!response.isSuccessful()) {
          throw new IOException("Unexpected code " + response);
        }

        Path file = Paths.get(fileName);
        Files.write(file, response.body().bytes());
      }
      
    }
  }

  public void fetchTask() throws ApiException, NoApiKeyException {
    String taskId = "your task id";
    ImageSynthesis is = new ImageSynthesis();
    // If set DASHSCOPE_API_KEY environment variable, apiKey can null.
    ImageSynthesisResult result = is.fetch(taskId, null);
    System.out.println(result.getOutput());
    System.out.println(result.getUsage());
  }

  public static void main(String[] args){
    try{
      basicCall();
    }catch(ApiException|NoApiKeyException | IOException e){
      System.out.println(e.getMessage());
    }
    System.exit(0);
  }
}

调用成功后，将会返回如下示例结果。

JSON

{
    "status_code": 200,
    "request_id": "ea8bfe77-2f35-9df3-ba47-7e05e917b3df",
    "code": null,
    "message": "",
    "output": {
        "task_id": "dea97660-9651-4e6b-a9c3-8afb325b28d0",
        "task_status": "SUCCEEDED",
        "results": [
            {
                "url": "url1"
            }
        ],
        "task_metrics": {
            "TOTAL": 1,
            "SUCCEEDED": 1,
            "FAILED": 0
        }
    },
    "usage": {
        "image_count": 1
    }
}

了解更多

有关StableDiffusion文生图模型API的详细调用文档可前往API详情页面进行了解。