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Remade-AI/ Dolly-Effect

2994runs
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API Sample: Remade-AI/Dolly-Effect

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>
      
                        

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

Dolly-Effect scale:

negative prompt

Your request will cost $0.00095 per second.

Running this model on Wiro costs approximately $0.47405 in total.

(Total cost varies depending on the request’s execution time.)
Remade-AI-Dolly-Effect-sample-1.mp4
Remade-AI-Dolly-Effect-sample-2.mp4
Remade-AI-Dolly-Effect-sample-3.mp4
Remade-AI-Dolly-Effect-sample-4.mp4
1744882806 Report This Model


Dolly 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 apply a Dolly zoom camera effect on any image subject! This model also works on T2V, with a very similar prompting style, although the I2V application is more robust.





Features



  • 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

d011Ye33ect dolly effect. The video begins with a close-up of the man’s steely gaze as he stands in a dusty cemetery, a cigar clenched in his mouth. The camera slowly zooms out, keeping his face centered while the background stretches—revealing crosses, gravestones, and the wide open desert behind him. The dolly effect intensifies the tension of the western standoff.




Prompt

d011Ye33ect dolly effect. The video begins with a close-up of the woman's face, her expression calm and confident. As the camera zooms out slowly, her poised figure in the iconic seated position is revealed. The background — a sterile, cold interrogation room — subtly distorts with the dolly effect, enhancing the intensity and drawing focus to her unwavering gaze.




Prompt

d011Ye33ect dolly effect. The video begins with a close-up of the man’s intense expression, his mouth open mid-shout. As the camera slowly zooms out, his battle stance and outstretched arms are revealed in full. The dolly effect causes the background of the ancient coliseum to shift and distort slightly, emphasizing the tension and power of the moment.




Prompt

d011Ye33ect dolly effect. The video starts with a close-up of the dogs’ faces as they share a single strand of spaghetti. The camera slowly zooms out, revealing the candlelit table, checkered tablecloth, and surrounding alleyway. The dolly effect keeps the dogs centered as the background stretches subtly, enhancing the intimacy of the moment.














Model File and Inference Workflow







📥 Download Links:



  • dolly_25_epochs.safetensors - LoRA Model File

  • wan_img2vid_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: d011Ye33ect dolly effect





Prompt Template


For prompting, check out the example prompts; this way of prompting seems to work very well.





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 2 minutes of video comprised of 40 short clips (each clip captioned separately) of various dolly effect scenes

  • Epochs: 25





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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