使用Java SDK调用AI写真的算法服务接口进行模型训练和写真制作。通过SDK，您可以定制LoRA模型，并根据模板制作写真。本文为您介绍使用Java SDK调用接口之前的准备工作以及使用示例。
前提条件
已准备好Java环境。
已准备好5-20张训练图片和1张模板图片，用于模型训练和写真制作。图片格式支持
.jpg、.jpeg、.png等。如果进行单人写真制作，模板图片中包含单张人脸即可。多张训练图片中的人脸属于同一个人。
如果进行多人写真制作，模板图片中需包含多张人脸，且人脸数量与模型训练的model_id数量一致。
请确保训练图片和模板图片的尺寸大于512×512像素。
准备工作
环境依赖：在Maven工程中使用aiservice SDK，必须在pom.xml文件<dependencies>中添加aiservice SDK的依赖，如下所示：
<dependency> <groupId>com.aliyun.openservices.aiservice</groupId> <artifactId>aiservice-sdk</artifactId> <version>1.0.1</version> </dependency>
初始化Client。
import com.aliyun.openservices.aiservice.ApiClient; public class AIGCImageTest { public static ApiClient apiClient; static { String host = 'HOST'; String appId = 'YOUR-APPID'; String token = 'YOUR-TOKEN'; apiClient = new ApiClient(host, appId, token); } }您需要根据实际情况，替换以下参数值。
参数
描述
<HOST>
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
开通AI写真后，您可以直接在AI写真页面查看Token。
更多详细内容，请参见github开源代码。
调用代码示例
AI写真是一个资源消耗量较大的服务，主要包括模型训练和写真制作两个环节。模型训练通常需要几分钟的响应时间，而写真制作则只需要数十秒即可完成。
核验请求（api.aigcImagesCheck）
请求代码示例如下：
import com.aliyun.openservices.aiservice.api; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.AIGCCreatRequest; import com.aliyun.openservices.aiservice.model.AIGCImageCheckResponse; import com.aliyun.openservices.aiservice.model.AIGCImageCreateResponse; import com.aliyun.openservices.aiservice.model.Response; import java.util.Arrays; import java.util.List; public class AIGCImageTest { public static ApiClient apiClient; private AigcImagesApi api ; static { String host = 'HOST'; String appId = 'YOUR-APPID'; String token = 'YOUR-TOKEN'; apiClient = new ApiClient(host, appId, token); api = new AigcImagesApi(apiClient); } public void aigcImagesCheckTest() throws Exception { // 输入训练的图片，以URL形式存在。 List<String> images =Arrays.asList( "https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg" ); AIGCImageCheckResponse response = api.aigcImagesCheck(images); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 AIGCImageCheckData data = response.getData(); // 返回检查结果code。 List<AIGCImageCheckResult> CheckResultsList = response.getCheckResults(); for(int i=0; i < CheckResultsList.size(); i++ ){ AIGCImageCheckResult result = CheckResultsList.get(i); Integer ResultCode = result.getCode(); System.out.println(ResultCode); } } }参数说明
参数名称
参数说明
images
图片URL列表，List<String>。
<HOST>
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
开通AI写真后，您可以直接在AI写真页面查看Token。
响应结果示例如下：
response 类型：AIGCImageCheckResponse
{ requestId: 3c0eeb6b-1faf-4dc4-8f9a-9a02486051c6 code: OK message: success data: AIGCImageCheckData{ requestId = '3c0eeb6b-1faf-4dc4-8f9a-9a02486051c6', images = ["https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg"], costTime=0.5460958480834961, checkResults=[ AIGCImageCheckResult{ code=1, frontal=false, url='https://xxx/0.jpg', message='success'}, AIGCImageCheckResult{ code=1, frontal=true, url='https://xxx/1.jpg', message='success'}, AIGCImageCheckResult{ code=4, frontal=false, url='https://xxx/2.jpg', message='Image detect error.'} ] } }检查返回值说明
参数名称
参数说明
类型
requestId
请求流水号。
String
code
请求状态码，取值如下：
OK：表示请求成功。
error：表示请求失败。
String
message
请求状态详细信息，成功为success，其他视具体返回内容。
String
data
返回数据详情。
AIGCImageCheckData
data字段说明： 类型 AIGCImageCheckData
参数名称
参数说明
类型
checkResults
代表输入每张图片的检测结果。 每个图片对应一个字典，每个字典一共有三个key，分别是url、message与frontal，分别代表图片的URL、图片检测详情和是否为正面。
List<AIGCImageCheckResult>
costTime
本次API花费的服务端计算时长。
float
images
核验的图片URL list。
List<String>
requestId
请求流水号（同上级request_id流水号）。
String
check_results的message汇总：
message
状态码
含义
success
1
代表符合要求。
Image decode error.
2
图像无法下载或者解码。
Number of face is not 1.
3
人脸数量不为1。
Image detect error.
4
人脸检测出错。
Image encoding error.
5
将人脸编码为特征向量时出错，表示无法检测到人脸。
This photo is not the same person in photos.
6
如果只出现了这个错误，表示多张图片中的人脸不属于同一个人。
发起模型训练（api.aigcImagesTrain）
请求代码示例如下：
package com.aliyun.aisdk; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.util.Arrays; import java.util.List; import java.io.IOException; public class AIGCImageTrain { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public void aigcImagesTrainTest() throws Exception { List<String> images =Arrays.asList( "https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg" ); AIGCImageTrainResponse response = api.aigcImagesTrain(images); int jobId = response.getData().getJobId(); String modelId = response.getData().getModelId(); String message = response.getMessage(); InlineResponse200Data Data = response.getData(); System.out.println(response); System.out.println(response.getMessage()); System.out.println("jobId:" + jobId); System.out.println("modelId:" + modelId); System.out.println("Data:" + Data); } }参数说明
参数位置
参数说明
images
图片URL列表，List<String>。
<HOST>
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
开通AI写真后，您可以直接在AI写真页面查看Token。
响应结果示例如下：
response 类型：AIGCImageTrainResponse
{ requestId: 2bd438df-2358-4852-b6b0-bf7d39b6dde7 code: OK message: success data: class InlineResponse200Data { jobId: xxxx modelId: xxxx-xxxxxxxx-xxxxxx } }
返回结果中的各字段说明如下：
参数名称
参数说明
类型
requestId
请求流水号。
String
code
请求状态码，是否完成，OK或者error。
String
message
请求状态详细信息，成功为success，其他视具体返回内容。
String
data
返回数据详情。
InlineResponse200Data
data字段说明
参数名称
参数说明
类型
jobId
任务ID。
int
modelId
本次模型训练的模型id，为一串长度为36的字符串编码。
String
需要获取job_id来进行训练结果查询；
需要获取model_id来对进行写真制作服务的请求。
训练结果查询（jobApi.getAsyncJob）
请求代码示例如下：
import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.util.Arrays; import java.util.List; import java.io.IOException; public class AIGCJobCheck { public void aigcJobStateGet() throws Exception { Integer jobId = new Integer(xxxx); // 异步任务ID AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); AsyncJobResponse jobResponse = jobApi.getAsyncJob(jobId); String request_id = jobResponse.getRequestId(); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); String Result_string = (String) job_data.get("job").getResult(); JsonObject jsonObject = new JsonParser().parse(Result_string).getAsJsonObject(); JsonArray result_states = jsonObject.get("states").getAsJsonArray(); for (int result_idx=0; result_idx < result_states.size(); result_idx++){ JsonObject result_one = result_states.get(result_idx).getAsJsonObject(); String result_url = result_one.get("url").getAsString(); } } }参数说明如下：
参数
类型
描述
jobId
Integer
训练任务ID。
响应结果示例如下：
当模型训练未执行完成时，响应结果如下：
{ requestId: 9a76c77d-c241-4691-8c93-fc6953fb668c code: OK message: success data: { job=AsyncJobData{ id=12746, appId='xxxxxxxxxx', state=1, message='model requesting', result="", requestId='111a6503-c2f7-4141-b17b-f8567e6a0a5f' } } }当模型训练执行完成后，响应结果如下：
{ requestId: 0fc513d1-5a9e-48e1-9b6f-2eca7c0b62e9 code: OK message: success data: { job=AsyncJobData{ id=12744, appId='xxxxxxxxxxx', state=2, message='success', result={ "cost_time":232.83351230621338, "model_id":"xxxxxxxxxxxx", "states":[{"code":1, "frontal":true, "message":"success", "url":"https://xxxx/train/1.jpg"}] }, requestId='83146ee3-68aa-40f7-b523-06f029e1db15' } } }
训练返回值说明
参数名称
参数说明
类型
requestId
请求流水号。
String
code
请求状态码，是否完成，OK或者error。
String
message
请求状态详细信息，成功为success，其他视具体返回内容。
String
data
返回数据详情。
Map<String, AsyncJobData>
data.get("job") 字段说明
参数名称
参数说明
类型
id
任务ID，即 job_id。
int
appId
用户的AppId。
String
state
任务状态码：
0：任务初始化。
1：任务执行中。
2：任务完成。
3：任务失败。
int
message
任务执行信息。
String
Result
模型返回结果。
String
Result模型返回结果说明 ： Result类型String
参数名称
参数说明
cost_time
本次训练消耗的总时间
states
每张图片核验的结果。
代表输入每张图片的检测结果。 每个图片对应一个字典，每个字典一共有三个key，分别是url、message与frontal，分别代表图片的URL、图片检测详情和是否为正面。
model_id
LoRA模型名称，同训练请求时获得的model_id。
LoRA模型名称，等同于训练请求时获得的model_id，用于写真制作时输入。
相关错误码说明
请求服务错误码如下：
HTTP状态码
code
message
说明
400
PARAMETER_ERROR
not found appid
appId填写错误。
401
PARAMETER_ERROR
sign error
token填写错误。
404
PARAMETER_ERROR
model not found
对应模型服务未部署。
结果查询的错误码如下：
HTTP状态码
code
message
说明
462
error
Invalid input data
输入数据解析错误。
462
error
Image not provided
并未提供训练图片。
462
error
Make dir in oss Error.
OSS创建文件夹失败，检查是否挂载OSS。
462
error
Image process error.
图像预处理出错。
469
error
Training - Not get best template image
训练异常退出导致没有生成参考图片。
469
error
Training - Not get lora weight
训练异常退出导致没有生成LoRA的权重。
写真制作
请求代码示例如下：
单人写真制作请求接口（api.aigcImagesCreate）
预测Stable Diffusion1.5
import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.io.FileOutputStream; import java.io.OutputStream; import java.util.Arrays; import java.util.List; import java.io.IOException; import sun.misc.BASE64Decoder; import sun.misc.BASE64Encoder; public class AIGCImageService { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public void aigcImageCreateGet() throws Exception { String modelId = "xxx-xxxx"; String templateImage = "https://xxxx.jpg"; String model_name = ""; Map<String, Object> configure = new TreeMap<String, Object>(); AIGCImageCreateResponse createResponse = api.aigcImagesCreate(modelId, templateImage, model_name, configure); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); // 生成图片的 base64 String imgStr = createResponse.getData().getImage(); BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } String imgFilePath = "test_single.jpg"; OutputStream out = new FileOutputStream(imgFilePath); out.write(imgBtyes); out.flush(); out.close(); } }参数说明如下：
参数
类型
描述
modelId
String
LoRA模型名称，需要输入训练获得的model-id。
当使用ipa_control_only模式时设置为""。
templateImage
String
模板的URL路径。
model_name
String
模型名称，默认输入空字符串。
configure
Map<String, Object>
模型返回配置，默认输入None。
<HOST>
String
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
String
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
String
开通AI写真后，您可以直接在AI写真页面查看Token。
预测Stable Diffusion XL
import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.io.FileOutputStream; import java.io.OutputStream; import java.util.Arrays; import java.util.List; import java.io.IOException; import sun.misc.BASE64Decoder; import sun.misc.BASE64Encoder; public class AIGCImageService { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public void aigcImageCreateGet() throws Exception { String modelId = "xxx-xxxx"; String templateImage = "https://xxxx.jpg"; String model_name = "create_xl"; Map<String, Object> configure = new TreeMap<String, Object>(); AIGCImageCreateResponse createResponse = api.aigcImagesCreate(modelId, templateImage, model_name, configure); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); // 生成图片的 base64 String imgStr = createResponse.getData().getImage(); BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } String imgFilePath = "test_single.jpg"; OutputStream out = new FileOutputStream(imgFilePath); out.write(imgBtyes); out.flush(); out.close(); } }参数说明如下：
参数位置
类型
参数说明
modelId
string
LoRA模型名称，需要输入训练获得的model-id。当使用ipa_control_only模式时设置为""。
templateImage
string
模板的URL路径。
当使用scene_lora或者prompt生成时设置为"t2i_generate"。
model_name
string
模型名称，使用Stable Diffusion XL则需要设置为create_xl。
configure
Map<String, Object>
模型返回配置configure，默认输入None。
多人写真制作请求接口（api.aigcImagesCreateByMultiModelIds）
import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.io.FileOutputStream; import java.io.OutputStream; import java.util.Arrays; import java.util.List; import java.io.IOException; import sun.misc.BASE64Decoder; import sun.misc.BASE64Encoder; public class AIGCImageService { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public void aigcImageCreateMulti() throws Exception { String[] modelIds = new String[]{"model-id1","model-id2"}; String templateImage = "https://xxxx.jpg"; String model_name = ""; Map<String, Object> configure = new TreeMap<String, Object>(); AIGCImageCreateResponse createResponse = api.aigcImagesCreateByMultiModelIds(model_id, template_image, model_name, config); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); // 生成图片的Base64 String imgStr = createResponse.getData().getImage(); BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { // 调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } String imgFilePath = "test_multi.jpg"; OutputStream out = new FileOutputStream(imgFilePath); out.write(imgBtyes); out.flush(); out.close(); } }参数
类型
描述
modelId
String
训练模型的model id。
templateImage
String
模板的URL路径。
model_name
String
模型名称，默认输入空字符串。
configure
Map<String, Object>
模型返回配置configure，默认输入None。
<HOST>
String
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
String
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
String
开通AI写真后，您可以直接在AI写真页面查看Token。
响应结果示例如下：
{ requestId: 5eb7741b-540b-4a5c-9c98-fdd1d714e51f code: OK message: success data: com.aliyun.openservices.aiservice.model.AIGCImageCreateData@358c99f5 }data字段说明：AIGCImageCreateData类型
参数名称
参数说明
类型
costTime
生成花费时间。
Float
image
图片base64。
String
写真制作返回的错误码说明
请求服务错误
HTTP状态码
code
message
说明
400
PARAMETER_ERROR
not found appid
appId填写错误
401
PARAMETER_ERROR
sign error
token填写错误
404
PARAMETER_ERROR
model not found
对应模型服务未部署
结果查询错误
HTTP状态码
code
message
说明
462
error
Invalid input data. Please check the input dict.
输入数据解析错误。
462
error
Image not provided. Please check the template_image.
并未提供写真制作的模板图片。
462
error
Prompts get error. Please check the model_id.
检查提供的model_id格式。
462
error
Face id image decord error. Pleace check the user's lora is trained or not.
用户上传的图片解码异常，请检查是否模型是否训练。
462
error
Roop image decord error. Pleace check the user's lora is trained or not.
Roop图像不存在，请检查模型是否训练。
462
error
Template image decode error. Please Give a new template.
模板图片解码错误，请给一张新的模板。
462
error
There is not face in template. Please Give a new template.
模板图像不存在人脸，请给一个新的模板。
462
error
Template image process error. Please Give a new template.
模板图片预处理错误，请提供一张新的模板图像。
469
error
First Face Fusion Error, Can't get face in template image.
第一次人脸融合出错。
469
error
First Stable Diffusion Process error. Check the webui status.
第一次Stable Diffusion处理出错
469
error
Second Face Fusion Error, Can't get face in template image.
第二次人脸融合出错。
469
error
Second Stable Diffusion Process error. Check the webui status.
第二次Stable Diffusion处理出错。
469
error
Please confirm if the number of faces in the template corresponds to the user ID.
请检查所给的user id数量与人脸数量是否相符。
469
error
Third Stable Diffusion Process error. Check the webui status.
背景处理出错，请更换模板。
端到端流程示例代码
端到端流程的代码示例如下。当代码执行成功后，会在当前目录生成AI写真制作图片。
常规链路（Stable Diffusion1.5）
package com.aliyun.aisdk; import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.model.*; import java.io.FileOutputStream; import java.io.OutputStream; import java.util.Arrays; import java.util.List; import java.io.IOException; import sun.misc.BASE64Decoder; import sun.misc.BASE64Encoder; public class AIGCImageRunner { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public byte[] base64ToBytes(String imgStr) throws IOException { BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } return imgBtyes; } public void aigcImagesCheck(List<String> images) throws Exception{ AIGCImageCheckResponse response = api.aigcImagesCheck(images); } public Object[] aigcImagesTrainRun(List<String> images) throws Exception { AIGCImageTrainResponse response = api.aigcImagesTrain(images); int jobId = response.getData().getJobId(); Object[] trainOut = new Object[2]; System.out.println(response); System.out.println(response.getMessage()); System.out.println("jobId:" + jobId); System.out.println("modelId:" + response.getData().getModelId()); System.out.println(response.getData()); trainOut[0]=jobId; trainOut[1] = response.getData().getModelId(); return trainOut; } public Integer aigcJobStateGet(AiServiceJobApi jobApi, int jobId_int) throws Exception { Integer jobId = new Integer(jobId_int); // 异步任务ID AsyncJobResponse jobResponse = jobApi.getAsyncJob(jobId); System.out.println(jobResponse.getData().get("job").getResult()); return jobResponse.getData().get("job").getState(); } public void CreateSingle(String modelId, String templateImage) throws Exception { AIGCImageCreateResponse createResponse = api.aigcImagesCreate(modelId, templateImage); // 生成图片的 base64 String imgStr = createResponse.getData().getImage(); System.out.println(createResponse.getData()); byte[] imgBtyes = base64ToBytes(imgStr); String imgFilePath = "test_single.jpg"; OutputStream out = new FileOutputStream(imgFilePath); out.write(imgBtyes); out.flush(); out.close(); } public void CreateMulti(String[] model_ids, String template_image)throws ApiException, IOException { String imgFilePath = "test_multi.jpg"; AIGCImageCreateResponse createResponse = api.aigcImagesCreateByMultiModelIds(model_ids, template_image, model_name, config); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, model_id is %s, request_id is %s\n",model_ids,request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream(output_image); out.write(image); out.flush(); out.close(); } } public void aigcEndtoEndCreate() throws Exception { List<String> images =Arrays.asList( "https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg" ); String templateImage = "https://xxx.jpg"; String multiTemplateImage = "https://xxx.jpg"; Object[] o = aigcImagesTrainRun(images); int jobId = (int)o[0]; String modelId = (String)o[1]; AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); while(true){ Integer jobState = aigcJobStateGet(jobApi, jobId); if (jobState == AsyncJobState.JOB_STATE_WAIT) { // job running System.out.println("job running"); } else if (jobState == AsyncJobState.JOB_STATE_SUCCESS) { System.out.println("job success"); break; } else { System.out.println("job fail"); throw new Exception("job fail"); } try { Thread.sleep(30000); } catch (InterruptedException e) { throw new RuntimeException(e); } } CreateSingle(modelId,templateImage); String[] modelIds = new String[]{modelId,modelId}; CreateMulti(model_ids, template_image) } }参数说明如下：
参数
描述
<HOST>
服务端地址：
http://ai-service.ce8cc13b6421545749e7b4605f3d02607.cn-hangzhou.alicontainer.com。<YOUR-APPID>
开通AI写真后，您可以直接在AI写真页面查看AppId。
<YOUR-TOKEN>
开通AI写真后，您可以直接在AI写真页面查看Token。
images
用于训练模型的图片URL地址，多个URL地址之间使用半角逗号（,）分隔。
templateImage
模板图片的URL地址，包含单张人脸。用于单人写真制作。
multiTemplateImage
模板图片的URL地址，包含多张人脸，且人脸数量和所给的model_id一致。用于多人写真制作。
常规链路（Stable Diffusion XL）
使用Stable Diffusion XL模型，首先需要联系PAI团队开通服务后，通过指定模型名称来进行使用。
package com.aliyun.aiservice.demo; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.model.*; import com.google.gson.JsonArray; import com.google.gson.JsonObject; import com.google.gson.JsonParser; import org.junit.Test; import sun.misc.BASE64Decoder; import java.io.FileOutputStream; import java.io.IOException; import java.io.OutputStream; import java.util.*; public class PhotoEndtoEndTest { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); public byte[] base64ToBytes(String imgStr) throws IOException { BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } return imgBtyes; } public boolean Check(List<String> images) throws ApiException { AIGCImageCheckResponse response = this.api.aigcImagesCheck(images); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 AIGCImageCheckData data = response.getData(); // 打印返回结果 boolean is_ok = false; if (!code.equals("OK")){ System.out.printf("aigc_images_check failed,request id is %\n", request_id); }else{ is_ok = true; System.out.printf("check images done, input %d images, return %d images, %d filtered by lvwang\n", images.size(),(data.getCheckResults().size()),(images.size()-data.getCheckResults().size())); for (int check_result_idx=0; check_result_idx < data.getCheckResults().size();check_result_idx++ ){ AIGCImageCheckResult checkResult = data.getCheckResults().get(check_result_idx); Integer checkResultCode = checkResult.getCode(); if (checkResultCode.equals(1)){ System.out.printf("check %s success\n", checkResult.getUrl()); }else { is_ok = false; System.out.printf("check %s failed, message is %s , request_id is %s\n", checkResult.getUrl(),checkResult.getMessage(),request_id); } } } return is_ok; } public Object[] Train(List<String> images, String model_name, Map<String, Object> config) throws ApiException { Integer job_id = -1; String model_id = ""; AIGCImageTrainResponse response = this.api.aigcImagesTrain(images,model_name,config); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 InlineResponse200Data Data = response.getData(); // 打印返回结果 if (!code.equals("OK")){ System.out.printf("aigc_images_train failed, request id is %s\n", request_id); }else{ job_id = response.getData().getJobId(); model_id = response.getData().getModelId(); System.out.printf("train job_id is %d, model id %s\n",job_id.intValue(),model_id); } Integer state = -1; while(true){ AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")){ System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); job_id = new Integer(-1); model_id = ""; }else{ state = job_data.get("job").getState(); if (state.equals(2)){ System.out.printf("model %s trained successfully\n",model_id); break; }else if(!state.equals(3)){ System.out.printf("training model %s\n",model_id); try { Thread.sleep(10000); } catch (InterruptedException e) { e.printStackTrace(); } }else{ System.out.printf("model %s trained failed, message: %s\n",model_id,job_message); break; } } } if (!state.equals(2)){ model_id = ""; } Object[] out = new Object[2]; out[0] = job_id; out[1] = model_id; return out; } public String[] QueryValidImageUrlsByJob(Integer job_id) throws ApiException { String[] image_urls = null; AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String request_id = jobResponse.getRequestId(); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")) { System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); }else { Integer state = job_data.get("job").getState(); if (state == 2){ System.out.printf("Job %s trained successfully\n", job_id); String Result_string = (String) job_data.get("job").getResult(); JsonObject jsonObject = new JsonParser().parse(Result_string).getAsJsonObject(); JsonArray result_states = jsonObject.get("states").getAsJsonArray(); image_urls = new String[result_states.size()]; for (int result_idx=0; result_idx < result_states.size(); result_idx++){ JsonObject result_one = result_states.get(result_idx).getAsJsonObject(); String result_url = result_one.get("url").getAsString(); image_urls[result_idx] = result_url; } }else{ System.out.printf("job %s not ready\n",job_id); } } return image_urls; } public boolean Create(String model_id, String template_image, String output_image, String model_name, Map<String, Object> config) throws IOException { System.out.println("Create"); AIGCImageCreateResponse createResponse = null; try{ createResponse = api.aigcImagesCreate(model_id, template_image, model_name, config); }catch (ApiException e){ System.out.println(); System.out.println(e.getResponseBody()); } System.out.println(createResponse); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, model_id is %s, request_id is %s\n",model_id,request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream(output_image); out.write(image); out.flush(); out.close(); } return true; } public boolean CreateMulti(String[] model_ids, String template_image, String output_image, String model_name, Map<String, Object> config) throws ApiException, IOException { AIGCImageCreateResponse createResponse = api.aigcImagesCreateByMultiModelIds(model_ids, template_image, model_name, config); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, model_id is %s, request_id is %s\n",model_ids,request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream(output_image); out.write(image); out.flush(); out.close(); } return true; } @Test public void aigcEndtoEndCreate() throws Exception { List<String> images = Arrays.asList( "https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg" ); String template_image = "https://xxx.jpg"; String multi_template_image = "https://xxx.jpg"; String model_name = "train_xl"; Map<String, Object> config = new HashMap<String, Object>(); Object[] train_out = Train(images, model_name, config); Integer job_id= (Integer) train_out[0]; String model_id = (String) train_out[1]; String[] model_ids = {model_id,model_id}; model_name = "create_xl"; //"" Map<String, Object> configure = new TreeMap<String, Object>(); Create(model_id, template_image,"single_out.jpg", model_name,configure); CreateMulti(model_ids, multi_template_image,"multi_out.jpg", model_name,configure); } }
通过单参考图进行AI写真制作（无需进行模型训练）
package com.aliyun.aiservice.demo; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.model.*; import com.google.gson.JsonArray; import com.google.gson.JsonObject; import com.google.gson.JsonParser; import org.junit.Test; import sun.misc.BASE64Decoder; import java.io.FileOutputStream; import java.io.IOException; import java.io.OutputStream; import java.util.*; public class PhotoReferCreateTest { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); public byte[] base64ToBytes(String imgStr) throws IOException { BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } return imgBtyes; } public boolean Check(List<String> images) throws ApiException { AIGCImageCheckResponse response = this.api.aigcImagesCheck(images); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 AIGCImageCheckData data = response.getData(); // 打印返回结果 boolean is_ok = false; if (!code.equals("OK")){ System.out.printf("aigc_images_check failed,request id is %\n", request_id); }else{ is_ok = true; System.out.printf("check images done, input %d images, return %d images, %d filtered by lvwang\n", images.size(),(data.getCheckResults().size()),(images.size()-data.getCheckResults().size())); for (int check_result_idx=0; check_result_idx < data.getCheckResults().size();check_result_idx++ ){ AIGCImageCheckResult checkResult = data.getCheckResults().get(check_result_idx); Integer checkResultCode = checkResult.getCode(); if (checkResultCode.equals(1)){ System.out.printf("check %s success\n", checkResult.getUrl()); }else { is_ok = false; System.out.printf("check %s failed, message is %s , request_id is %s\n", checkResult.getUrl(),checkResult.getMessage(),request_id); } } } return is_ok; } public boolean Create(String template_image, String output_image, String ref_image) throws IOException { System.out.println("Create"); AIGCImageCreateResponse createResponse = null; Map<String, Object> config = new TreeMap<String, Object>(); config.put("ipa_control_only",true); config.put("ipa_weight",0.6); config.put("ipa_image_path",ref_image); try{ createResponse = api.aigcImagesCreate("", template_image, "", config); }catch (ApiException e){ System.out.println(); System.out.println(e.getResponseBody()); } System.out.println(createResponse); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, model_id is %s, request_id is %s\n",model_id,request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream(output_image); out.write(image); out.flush(); out.close(); } return true; } @Test public void aigcEndtoEndCreate() throws Exception { String template_image = "https://demo.jpg"; String ref_image = "https://reference.jpg"; Create(template_image,"ref_out.jpg", ref_image); } }通过提示词和单参考图生成模板图像进行AI写真制作（无需提供模板图像）
package com.aliyun.aiservice.demo; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.model.*; import com.google.gson.JsonArray; import com.google.gson.JsonObject; import com.google.gson.JsonParser; import org.junit.Ignore; import org.junit.Test; import sun.misc.BASE64Decoder; import java.io.FileOutputStream; import java.io.IOException; import java.io.OutputStream; import java.util.*; public class PhotoPtomptCreateTest { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); public byte[] base64ToBytes(String imgStr) throws IOException { BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } return imgBtyes; } public boolean Check(List<String> images) throws ApiException { AIGCImageCheckResponse response = this.api.aigcImagesCheck(images); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 AIGCImageCheckData data = response.getData(); // 打印返回结果 boolean is_ok = false; if (!code.equals("OK")){ System.out.printf("aigc_images_check failed,request id is %\n", request_id); }else{ is_ok = true; System.out.printf("check images done, input %d images, return %d images, %d filtered by lvwang\n", images.size(),(data.getCheckResults().size()),(images.size()-data.getCheckResults().size())); for (int check_result_idx=0; check_result_idx < data.getCheckResults().size();check_result_idx++ ){ AIGCImageCheckResult checkResult = data.getCheckResults().get(check_result_idx); Integer checkResultCode = checkResult.getCode(); if (checkResultCode.equals(1)){ System.out.printf("check %s success\n", checkResult.getUrl()); }else { is_ok = false; System.out.printf("check %s failed, message is %s , request_id is %s\n", checkResult.getUrl(),checkResult.getMessage(),request_id); } } } return is_ok; } public Object[] Train(List<String> images, String model_name, Map<String, Object> config) throws ApiException { Integer job_id = -1; String model_id = ""; AIGCImageTrainResponse response = this.api.aigcImagesTrain(images,model_name,config); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 InlineResponse200Data Data = response.getData(); // 打印返回结果 if (!code.equals("OK")){ System.out.printf("aigc_images_train failed, request id is %s\n", request_id); }else{ job_id = response.getData().getJobId(); model_id = response.getData().getModelId(); System.out.printf("train job_id is %d, model id %s\n",job_id.intValue(),model_id); } Integer state = -1; while(true){ AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")){ System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); job_id = new Integer(-1); model_id = ""; }else{ state = job_data.get("job").getState(); if (state.equals(2)){ System.out.printf("model %s trained successfully\n",model_id); break; }else if(!state.equals(3)){ System.out.printf("training model %s\n",model_id); try { Thread.sleep(10000); } catch (InterruptedException e) { e.printStackTrace(); } }else{ System.out.printf("model %s trained failed, message: %s\n",model_id,job_message); break; } } } if (!state.equals(2)){ model_id = ""; } Object[] out = new Object[2]; out[0] = job_id; out[1] = model_id; return out; } public String[] QueryValidImageUrlsByJob(Integer job_id) throws ApiException { String[] image_urls = null; AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String request_id = jobResponse.getRequestId(); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")) { System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); }else { Integer state = job_data.get("job").getState(); if (state == 2){ System.out.printf("Job %s trained successfully\n", job_id); String Result_string = (String) job_data.get("job").getResult(); JsonObject jsonObject = new JsonParser().parse(Result_string).getAsJsonObject(); JsonArray result_states = jsonObject.get("states").getAsJsonArray(); image_urls = new String[result_states.size()]; for (int result_idx=0; result_idx < result_states.size(); result_idx++){ JsonObject result_one = result_states.get(result_idx).getAsJsonObject(); String result_url = result_one.get("url").getAsString(); image_urls[result_idx] = result_url; } }else{ System.out.printf("job %s not ready\n",job_id); } } return image_urls; } public boolean Create(String model_id, String t2i_prompt, String template_image) throws IOException { System.out.println("Create"); AIGCImageCreateResponse createResponse = null; HashMap<String,Object> config = new HashMap<String, Object>(); config.put("t2i_prompt", t2i_prompt); try{ createResponse = api.aigcImagesCreate(model_id, template_image, "", config); }catch (ApiException e){ System.out.println(); System.out.println(e.getResponseBody()); } System.out.println(createResponse); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, model_id is %s, request_id is %s\n",model_id,request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream("prompt_out.jpg"); out.write(image); out.flush(); out.close(); } return true; } @Test public void aigcEndtoEndCreate() throws Exception { List<String> images =Arrays.asList( "https://xxx/0.jpg", "https://xxx/1.jpg", "https://xxx/2.jpg" ); String template_image = "https://demo.jpg"; String model_name = ""; Map<String, Object> config = new HashMap<String, Object>(); Object[] train_out = Train(images, model_name, config); Integer job_id= (Integer) train_out[0]; String model_id = (String) train_out[1]; String t2i_prompt = "(portrait:1.5), 1girl, bokeh, bouquet, brown_hair, cloud, flower, hairband, hydrangea, lips, long_hair, outdoors, sunlight, white_flower, white_rose, green sweater, sweater, (cloth:1.0), (best quality), (realistic, photo-realistic:1.3), film photography, minor acne, (portrait:1.1), (indirect lighting), extremely detailed CG unity 8k wallpaper, huge filesize, best quality, realistic, photo-realistic, ultra high res, raw photo, put on makeup"; Create(model_id, t2i_prompt, template_image); } }通过提示词和单参考图生成模板图像进行AI写真制作（无需提供模板图像和进行模型训练）
package com.aliyun.aiservice.demo; import com.aliyun.openservices.aiservice.ApiClient; import com.aliyun.openservices.aiservice.ApiException; import com.aliyun.openservices.aiservice.api.AiServiceJobApi; import com.aliyun.openservices.aiservice.api.AigcImagesApi; import com.aliyun.openservices.aiservice.model.*; import com.google.gson.JsonArray; import com.google.gson.JsonObject; import com.google.gson.JsonParser; import org.junit.Ignore; import org.junit.Test; import sun.misc.BASE64Decoder; import java.io.FileOutputStream; import java.io.IOException; import java.io.OutputStream; import java.util.*; public class PhotoPtomptCreateTest { public String host = 'HOST'; public String appId = 'YOUR-APPID'; public String token = 'YOUR-TOKEN'; public ApiClient apiClient = new ApiClient(host, appId, token); public AigcImagesApi api = new AigcImagesApi(apiClient); public AiServiceJobApi jobApi = new AiServiceJobApi(apiClient); public byte[] base64ToBytes(String imgStr) throws IOException { BASE64Decoder decoder = new BASE64Decoder(); byte[] imgBtyes = decoder.decodeBuffer(imgStr); for (int i = 0; i < imgBtyes.length; ++i) { //调整异常数据 if (imgBtyes[i] < 0) { imgBtyes[i] += 256; } } return imgBtyes; } public boolean Check(List<String> images) throws ApiException { AIGCImageCheckResponse response = this.api.aigcImagesCheck(images); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 AIGCImageCheckData data = response.getData(); // 打印返回结果 boolean is_ok = false; if (!code.equals("OK")){ System.out.printf("aigc_images_check failed,request id is %\n", request_id); }else{ is_ok = true; System.out.printf("check images done, input %d images, return %d images, %d filtered by lvwang\n", images.size(),(data.getCheckResults().size()),(images.size()-data.getCheckResults().size())); for (int check_result_idx=0; check_result_idx < data.getCheckResults().size();check_result_idx++ ){ AIGCImageCheckResult checkResult = data.getCheckResults().get(check_result_idx); Integer checkResultCode = checkResult.getCode(); if (checkResultCode.equals(1)){ System.out.printf("check %s success\n", checkResult.getUrl()); }else { is_ok = false; System.out.printf("check %s failed, message is %s , request_id is %s\n", checkResult.getUrl(),checkResult.getMessage(),request_id); } } } return is_ok; } public Object[] Train(List<String> images, String model_name, Map<String, Object> config) throws ApiException { Integer job_id = -1; String model_id = ""; AIGCImageTrainResponse response = this.api.aigcImagesTrain(images,model_name,config); // 请求流水号 String request_id = response.getRequestId(); // 请求状态 String code = response.getCode(); // 请求状态具体信息 String message = response.getMessage(); // 请求返回内容 InlineResponse200Data Data = response.getData(); // 打印返回结果 if (!code.equals("OK")){ System.out.printf("aigc_images_train failed, request id is %s\n", request_id); }else{ job_id = response.getData().getJobId(); model_id = response.getData().getModelId(); System.out.printf("train job_id is %d, model id %s\n",job_id.intValue(),model_id); } Integer state = -1; while(true){ AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")){ System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); job_id = new Integer(-1); model_id = ""; }else{ state = job_data.get("job").getState(); if (state.equals(2)){ System.out.printf("model %s trained successfully\n",model_id); break; }else if(!state.equals(3)){ System.out.printf("training model %s\n",model_id); try { Thread.sleep(10000); } catch (InterruptedException e) { e.printStackTrace(); } }else{ System.out.printf("model %s trained failed, message: %s\n",model_id,job_message); break; } } } if (!state.equals(2)){ model_id = ""; } Object[] out = new Object[2]; out[0] = job_id; out[1] = model_id; return out; } public String[] QueryValidImageUrlsByJob(Integer job_id) throws ApiException { String[] image_urls = null; AsyncJobResponse jobResponse = this.jobApi.getAsyncJob(job_id); String request_id = jobResponse.getRequestId(); String job_code = jobResponse.getCode(); String job_message = jobResponse.getMessage(); Map<String, AsyncJobData> job_data = jobResponse.getData(); if (!job_code.equals("OK")) { System.out.printf("get_async_job failed, request id is %s, message is %s\n", request_id, job_message); }else { Integer state = job_data.get("job").getState(); if (state == 2){ System.out.printf("Job %s trained successfully\n", job_id); String Result_string = (String) job_data.get("job").getResult(); JsonObject jsonObject = new JsonParser().parse(Result_string).getAsJsonObject(); JsonArray result_states = jsonObject.get("states").getAsJsonArray(); image_urls = new String[result_states.size()]; for (int result_idx=0; result_idx < result_states.size(); result_idx++){ JsonObject result_one = result_states.get(result_idx).getAsJsonObject(); String result_url = result_one.get("url").getAsString(); image_urls[result_idx] = result_url; } }else{ System.out.printf("job %s not ready\n",job_id); } } return image_urls; } public boolean Create(String t2i_prompt, String template_image, String ref_image) throws IOException { System.out.println("Create"); AIGCImageCreateResponse createResponse = null; HashMap<String,Object> config = new HashMap<String, Object>(); config.put("t2i_prompt", t2i_prompt); config.put("ipa_control_only", true); config.put("ipa_weight", 0.6); config.put("ipa_image_path", ref_image); try{ createResponse = api.aigcImagesCreate("", template_image, "", config); }catch (ApiException e){ System.out.println(); System.out.println(e.getResponseBody()); } System.out.println(createResponse); // 请求流水号 String request_id = createResponse.getRequestId(); // 请求状态 String code = createResponse.getCode(); // 请求状态具体信息 String message = createResponse.getMessage(); // 请求返回内容 AIGCImageCreateData data = createResponse.getData(); if (!code.equals("OK")){ System.out.printf("aigc_images_create failed, request_id is %s\n",request_id); }else { String imgStr = createResponse.getData().getImage(); byte[] image = base64ToBytes(imgStr); OutputStream out = new FileOutputStream("ref_prompt_out.jpg"); out.write(image); out.flush(); out.close(); } return true; } @Test public void aigcEndtoEndCreate() throws Exception { String template_image = "https://demo.jpg"; String ref_image = "https://reference.jpg"; String t2i_prompt = "(portrait:1.5), 1girl, bokeh, bouquet, brown_hair, cloud, flower, hairband, hydrangea, lips, long_hair, outdoors, sunlight, white_flower, white_rose, green sweater, sweater, (cloth:1.0), (best quality), (realistic, photo-realistic:1.3), film photography, minor acne, (portrait:1.1), (indirect lighting), extremely detailed CG unity 8k wallpaper, huge filesize, best quality, realistic, photo-realistic, ultra high res, raw photo, put on makeup"; Create(t2i_prompt, template_image, ref_image); } }
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