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LordJia/ LeLo - LEGO v2.1
Copy Prompt for LLM

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1583runs
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API Sample: LordJia/LeLo - LEGO v2.1

You don't have any projects yet. To be able to use our api service effectively, please sign in/up and create a project.

Get your api key
  • curl
  • nodejs
  • csharp
  • php
  • swift
  • dart
  • kotlin
  • go
  • python

Prepare Authentication Signature

                          
  //Sign up Wiro dashboard and create project
  export YOUR_API_KEY="{{useSelectedProjectAPIKey}}"; 
  export YOUR_API_SECRET="XXXXXXXXX"; 

   //unix time or any random integer value
  export NONCE=$(date +%s);

  //hmac-SHA256 (YOUR_API_SECRET+Nonce) with YOUR_API_KEY
  export SIGNATURE="$(echo -n "${YOUR_API_SECRET}${NONCE}" | openssl dgst -sha256 -hmac "${YOUR_API_KEY}")";
      
                        

Create a New Folder - Make HTTP Post Request

Create a New Folder - Response

Upload a File to the Folder - Make HTTP Post Request

Upload a File to the Folder - Response

Run Command - Make HTTP Post Request

                          
  curl -X POST "{{apiUrl}}/Run/{{toolSlugOwner}}/{{toolSlugProject}}"  \
  -H "Content-Type: {{contentType}}" \
  -H "x-api-key: ${YOUR_API_KEY}" \
  -H "x-nonce: ${NONCE}" \
  -H "x-signature: ${SIGNATURE}" \
  -d '{{toolParameters}}';

      
                        

Run Command - Response

                          
  //response body
  {
      "errors": [],
      "taskid": "2221",
      "socketaccesstoken": "eDcCm5yyUfIvMFspTwww49OUfgXkQt",
      "result": true
  }
      
                        

Get Task Detail - Make HTTP Post Request

                          
  curl -X POST "{{apiUrl}}/Task/Detail"  \
  -H "Content-Type: {{contentType}}" \
  -H "x-api-key: ${YOUR_API_KEY}" \
  -H "x-nonce: ${NONCE}" \
  -H "x-signature: ${SIGNATURE}" \
  -d '{
    "tasktoken": 'eDcCm5yyUfIvMFspTwww49OUfgXkQt',
  }';

      
                        

Get Task Detail - Response

                          
  //response body
  {
    "total": "1",
    "errors": [],
    "tasklist": [
        {
            "id": "2221",
            "uuid": "15bce51f-442f-4f44-a71d-13c6374a62bd",
            "name": "",
            "socketaccesstoken": "eDcCm5yyUfIvMFspTwww49OUfgXkQt",
            "parameters": {
                "inputImage": "https://api.wiro.ai/v1/File/mCmUXgZLG1FNjjjwmbtPFr2LVJA112/inputImage-6060136.png"
            },
            "debugoutput": "",
            "debugerror": "",
            "starttime": "1734513809",
            "endtime": "1734513813",
            "elapsedseconds": "6.0000",
            "status": "task_postprocess_end",
            "cps": "0.000585000000",
            "totalcost": "0.003510000000",
            "guestid": null,
            "projectid": "699",
            "modelid": "598",
            "description": "",
            "basemodelid": "0",
            "runtype": "model",
            "modelfolderid": "",
            "modelfileid": "",
            "callbackurl": "",
            "marketplaceid": null,
            "createtime": "1734513807",
            "canceltime": "0",
            "assigntime": "1734513807",
            "accepttime": "1734513807",
            "preprocessstarttime": "1734513807",
            "preprocessendtime": "1734513807",
            "postprocessstarttime": "1734513813",
            "postprocessendtime": "1734513814",
            "pexit": "0",
            "categories": "["tool","image-to-image","quick-showcase","compare-landscape"]",
            "outputs": [
                {
                    "id": "6bc392c93856dfce3a7d1b4261e15af3",
                    "name": "0.png",
                    "contenttype": "image/png",
                    "parentid": "6c1833f39da71e6175bf292b18779baf",
                    "uuid": "15bce51f-442f-4f44-a71d-13c6374a62bd",
                    "size": "202472",
                    "addedtime": "1734513812",
                    "modifiedtime": "1734513812",
                    "accesskey": "dFKlMApaSgMeHKsJyaDeKrefcHahUK",
                    "foldercount": "0",
                    "filecount": "0",
                    "ispublic": 0,
                    "expiretime": null,
                    "url": "https://cdn1.wiro.ai/6a6af820-c5050aee-40bd7b83-a2e186c6-7f61f7da-3894e49c-fc0eeb66-9b500fe2/0.png"
                }
            ],
            "size": "202472"
        }
    ],
    "result": true
  }
      
                        

Get Task Process Information and Results with Socket Connection

                          
<script type="text/javascript">
  window.addEventListener('load',function() {
    //Get socketAccessToken from task run response
    var SocketAccessToken = 'eDcCm5yyUfIvMFspTwww49OUfgXkQt';
    WebSocketConnect(SocketAccessToken);
  });

  //Connect socket with connection id and register task socket token
  async function WebSocketConnect(accessTokenFromAPI) {
    if ("WebSocket" in window) {
        var ws = new WebSocket("wss://socket.wiro.ai/v1");
        ws.onopen = function() {  
          //Register task socket token which has been obtained from task run API response
          ws.send('{"type": "task_info", "tasktoken": "' + accessTokenFromAPI + '"}'); 
        };

        ws.onmessage = function (evt) { 
          var msg = evt.data;

          try {
              var debugHtml = document.getElementById('debug');
              debugHtml.innerHTML = debugHtml.innerHTML + "\n" + msg;

              var msgJSON = JSON.parse(msg);
              console.log('msgJSON: ', msgJSON);

              if(msgJSON.type != undefined)
              {
                console.log('msgJSON.target: ',msgJSON.target);
                switch(msgJSON.type) {
                    case 'task_queue':
                      console.log('Your task has been waiting in the queue.');
                    break;
                    case 'task_accept':
                      console.log('Your task has been accepted by the worker.');
                    break;
                    case 'task_preprocess_start':
                      console.log('Your task preprocess has been started.');
                    break;
                    case 'task_preprocess_end':
                      console.log('Your task preprocess has been ended.');
                    break;
                    case 'task_assign':
                      console.log('Your task has been assigned GPU and waiting in the queue.');
                    break;
                    case 'task_start':
                      console.log('Your task has been started.');
                    break;
                    case 'task_output':
                      console.log('Your task has been started and printing output log.');
                      console.log('Log: ', msgJSON.message);
                    break;
                    case 'task_error':
                      console.log('Your task has been started and printing error log.');
                      console.log('Log: ', msgJSON.message);
                    break;
                   case 'task_output_full':
                      console.log('Your task has been completed and printing full output log.');
                    break;
                    case 'task_error_full':
                      console.log('Your task has been completed and printing full error log.');
                    break;
                    case 'task_end':
                      console.log('Your task has been completed.');
                    break;
                    case 'task_postprocess_start':
                      console.log('Your task postprocess has been started.');
                    break;
                    case 'task_postprocess_end':
                      console.log('Your task postprocess has been completed.');
                      console.log('Outputs: ', msgJSON.message);
                      //output files will add ui
                      msgJSON.message.forEach(function(currentValue, index, arr){
                          console.log(currentValue);
                          var filesHtml = document.getElementById('files');
                          filesHtml.innerHTML = filesHtml.innerHTML + '<img src="' + currentValue.url + '" style="height:300px;">'
                      });
                    break;
                }
              }
          } catch (e) {
            console.log('e: ', e);
            console.log('msg: ', msg);
          }
        };

        ws.onclose = function() { 
          alert("Connection is closed..."); 
        };
    } else {              
        alert("WebSocket NOT supported by your Browser!");
    }
  }
</script>
      
                        

Prepare UI Elements Inside Body Tag

                          
  <div id="files"></div>
  <pre id="debug"></pre>
      
                        
Trigger Word(s):

Tell us about any details you want to generate

LeLo-LEGO-v2.1 scale:

Specify things to not see in the output

1694361402 Report This Model

Initial version for SDXL 1.0

LeLo stands for LEGO LoRA. It is a LoRA trained with over 900 images from the LEGO MiniFigures, BrickHeadz, and Creator themes. It provides a simulation of the LEGO design style.

Update:

The V2.0_SDXL1.0 version is trained based on the SDXL 1.0 base model. Recommended resolutions include 1024x1024, 912x1144, 888x1176, and 840x1256.

If you are using the SD 1.5 series base models, you could use the V2.0_SD1.5_768p or V2.0_SD1.5_512p version. The difference between the two versions is the resolution of the training images (768x768 and 512x512 respectively).

Usage:

Trigger words:

LEGO MiniFig, {prompt}: MiniFigures theme, suitable for human figures and anthropomorphic animal images.

LEGO BrickHeadz, {prompt}: BrickHeadz theme, suitable for human figures and anthropomorphic animal images.

LEGO Creator, {prompt}: Creator theme, widely applicable to objects, animals, plants, buildings, etc.

LoRA Weight: Between 0.6-1.0, recommended to use 0.8.

Denoising: Recommended to use 0.3.

Base model: The V2.0_SDXL1.0 version should be paired with the SDXL 1.0 series base models. For the SD 1.5 versions, it is recommended to use it in conjunction with the Realistic Vision series base models, but you can also try pairing it with other base models to explore different styles.

If you appreciate my work, please leave your feedback, it is of great importance to me. Additionally, feel free to explore my other LoRA creations.

LeLo 为 LEGO LoRA 简称。此 LoRA 模型使用了 900 多张 LEGO MiniFigures 系列,BrickHeadz 系列,和 Creator 系列产品图片训练而成,提供对乐高设计风格的模拟。

更新说明:

V2.0_SDXL1.0 版基于 SDXL 1.0 基础模型训练。使用此版本 LoRA 生成图片时分辨率推荐使用 1024x1024,912x1144,888x1176,840x1256 等。

如果你在使用 SD 1.5 系列的基础模型,可选择使用 V2.0_SD1.5_768p 或 V2.0_SD1.5_512p 版本。两个版本的区别是训练图像的分辨率(分别为 768x768 和 512x512)。

使用方法:

触发词(Trigger words):

LEGO MiniFig, {prompt}:人仔风格,适用于人物形象和拟人的动物形象。

LEGO BrickHeadz, {prompt}:方头仔风格,适用于人物形象和拟人的动物形象。

LEGO Creator, {prompt}: 乐高创意系列风格,广泛适用于物品、动物、植物、建筑等。

LoRA 权重(weight):0.6-1.0 之间,推荐使用 0.8

放大重绘幅度(Denoising):推荐使用 0.3

基础模型:V2.0_SDXL1.0 版请配合 SDXL 1.0 系列基础模型。SD 1.5 版本推荐与 Realistic Vision 系列基础模型配合使用,也可尝试与其他基础模型配合使用以探索不同的风格。

如果你喜欢我的工作,请留下你的评价,这对我非常重要。另外,也欢迎浏览了解我其他的 LoRA 作品。

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