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Remade-AI/ Painting

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API Sample: Remade-AI/Painting

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
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  • kotlin
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  • 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>
      
                        

Choose an image that will re-generate

Choose an image URL that will re-generate

Trigger Word(s):

Tell us about any details you want to generate

Painting scale:

negative prompt

Your request will cost $0.00095 per second.

(Total cost varies depending on the request’s execution time.)
Remade-AI-Painting-sample-1.mp4
Remade-AI-Painting-sample-2.mp4
Remade-AI-Painting-sample-3.mp4
1744881332 Report This Model


Historic Painting Effect LoRA for Wan2.1 14B I2V 480p




Overview


This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to make any person/object in an image become a historic painting version of themselves!





Features



  • Transform any image into a video of the subject as a historic painting version of themselves!

  • Trained on the Wan2.1 14B 480p I2V base model

  • Consistent results across different object types

  • Simple prompt structure that's easy to adapt





Community



  • Discord: Join our community to generate videos with this LoRA for free

  • Request LoRAs: We're training and open-sourcing Wan2.1 LoRAs for free - join our Discord to make requests!






Prompt

The video starts with a dog. The next scene shows the dog inside a gold framed mirror in a room. The dog is wearing the same clothes and the background outside the frame shows a landscape scene. Next, the dog transforms into a p41nt1ng painting frame it, where the dog is dressed in red and blue clothing. The background is an older style painting scene. Finally, the dog is in another p41nt1ng painting frame it, where it is dressed in a red robe with the same landscape background in the painting.




Prompt

The video starts with a woman. The next scene shows the woman inside a gold framed mirror in a room. She is wearing the same clothes and the background outside the frame shows a landscape scene. Next, the woman transforms into a p41nt1ng painting frame it, where she is dressed in red and blue clothing. The background is an older style painting scene. Finally, the woman is in another p41nt1ng painting frame it, where she is dressed in a red robe with the same landscape background in the painting.




Prompt

The video starts with a man. The next scene shows the man inside a gold framed mirror in a room. He is wearing the same clothes and the background outside the frame shows a landscape scene. Next, the man transforms into a p41nt1ng painting frame it, where he is dressed in red and blue clothing. The background is an older style painting scene. Finally, the man is in another p41nt1ng painting frame it, where he is dressed in a red robe with the same landscape background in the painting.















Model File and Inference Workflow







📥 Download Links:



  • painting_50_epochs.safetensors - LoRA Model File

  • wan_I2V_LoRA_workflow.json - Wan I2V with LoRA Workflow for ComfyUI






Recommended Settings



  • LoRA Strength: 1.0

  • Embedded Guidance Scale: 6.0

  • Flow Shift: 5.0





Trigger Words


The key trigger phrase is: p41nt1ng painting frame it





Prompt Template


For best results, try following the structure of the prompt examples above. These worked well for me.





ComfyUI Workflow


This LoRA works with a modified version of Kijai's Wan Video Wrapper workflow. The main modification is adding a Wan LoRA node connected to the base model.


See the Downloads section above for the modified workflow.







Model Information


The model weights are available in Safetensors format. See the Downloads section above.





Training Details



  • Base Model: Wan2.1 14B I2V 480p

  • Training Data: Trained on 35 seconds of video comprised of 7 short clips (each clip captioned separately) of people transforming into a painting version of themselves!

  • Epochs: 50





Additional Information


Training was done using Diffusion Pipe for Training





Acknowledgments


Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!



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