Welcome
Remade-AI/ Jungle
API Sample: Remade-AI/Jungle
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Get your api keyPrepare 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>
Jungle 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 jungle version of themselves!
Features
- Transform any image into a video of the subject as a jungle Tarzan 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 begins with a portrait of a man. The background is blurry, with shades of grey and green. Next, the 1ung13 jungle transformation occurs. The man is now in a jungle setting, bathed in sunlight. His hair is longer, and his hair is up. He is shirtless, with tribal markings on his chest. He wears jungle-like shorts. The man is swinging from a vine, posing in a dynamic, action-oriented manner. A dark panther-like figure is in the background. The scene evokes a sense of adventure and the wild.
- Prompt
- The video begins with a portrait of a man. The background is blurry, with shades of grey and green. Next, the 1ung13 jungle transformation occurs. The man is now in a jungle setting, bathed in sunlight. His hair is longer, and his hair is up. He is shirtless, with tribal markings on his chest. He wears jungle-like shorts. The man is swinging from a vine, posing in a dynamic, action-oriented manner. A dark panther-like figure is in the background. The scene evokes a sense of adventure and the wild.
Model File and Inference Workflow
📥 Download Links:
- jungle_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: 1ung13 jungle transformation
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 their jungle version!
- Epochs: 50
Acknowledgments
Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!
Tools
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