Welcome
RadioMood/ Textureful
API Sample: RadioMood/Textureful
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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>
My attempt at making a 2D anime model that has a nice, textured look to it. Admittedly, it's not something I spent a whole lot of time on - mostly because I didn't use cosine merges or anything, just model block weights - but I do think it turned out pretty good.
Disclaimer: This model is intentionally focused on generating women and is slightly NSFW leaning - which shouldn't come as much of a shock since that's the majority of models on CivitAI. If you're not using it for that, your mileage may vary.
Usage Guidelines:
(Note: Updated for v2)
Positive Prompt:
Start with "(masterpiece, highest quality, illustration)". Add "comics style" and/or "thick outlines" to emphasize outlines - note that this also leads to more exaggerated female proportions. "arms by side" helps with keeping messed-up hands out of portraits.
Negative Prompt:
"(worst quality, low quality:1.4), poorly drawn, bad anatomy, wrong anatomy, blurry, open mouth" seems to work best for most things; you may occasionally need to prompt against other things.
VAE:
I just use vae-ft-mse-840000-ema-pruned.
Sampler:
DPM ++2M, 25 steps, discard penultimate sigma, karras sigma algorithm
CFG 6-8 (The example images all use 7, but you may have to tweak to get the eyes right)
Upscaler Model:
ESGRAN 4x-AnimeSharp seems to be the best choice for getting that textured look. ESGRAN 8x-Superscale can emphasize the textured look but is messier. ESGRAN-4xAnime-6B produces a flatter look.
Upscaler Settings:
10 steps, 0.5 strength, 2x upscale, secondary sampler DPM 2M++ (if your webui gives you that option)
Adetailer:
For faces, use face_yolo8vn with no prompting. If you want to use it for hands as well, use hand_yolo8vn with "hand" in the positive prompt - I don't usually use Adetailer on hands since it doesn't work that well.
Formula:
V2:
Part A: Argent - v1.0 + Occidental Mix - v3.0 with GradV MBW Preset
Part B: Kilkenny Mix - v1.0 + Part A with Cosine MBW Preset
Part C: Part B + Arthemy Comics - v 7.0 with MBW [0,0,0,0,0,0,0.1,1,1,1,1,1,0,0,0,1,1,1,1,1,0.1,0,0,0,0,0]
Mix: Part C + Part B with Ring08_5 MBW Preset
V1:
Part A: Argent - v1.0 + Occidental Mix - v3.0 with GradV MBW Preset
Part B: Arthemy Comics - v6.0 + Part A with Ring10_03 MBW Preset
Part C: Part B + Kilkenny Mix - v1.0 with Cosine MBW Preset
Mix: Part B + Part C with Ring10_03 MBW Preset
(Note, MBW stands for Model Block Weights)
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