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diffusers /
controlnet-canny-sdxl-1.0
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controlnet-canny-sdxl-1.0

SDXL ControlNet is a neural network structure to control diffusion models by adding extra conditions.

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  • API Integration Guide

API Sample: diffusers/controlnet-canny-sdxl-1.0

📚 For LLM Integration:

For complete parameter details and examples, please also review the markdown documentation at:
/models/diffusers/controlnet-canny-sdxl-1-0/llms.txt
/models/diffusers/controlnet-canny-sdxl-1-0/llms-full.txt

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  • swift
  • dart
  • kotlin
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  • python

Prepare Authentication (Signature)

                            //Sign up Wiro dashboard and create project
export YOUR_API_KEY="YOUR_WIRO_API_KEY";
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 (Multipart)

                          
# ⚠️ IMPORTANT: Remove all commented lines (starting with #) before running
# Bash doesn't support comments in command continuation (lines ending with \)

curl -X POST "https://api.wiro.ai/v1/Run/diffusers/controlnet-canny-sdxl-1-0"  \
-H "x-api-key: ${YOUR_API_KEY}" \
-H "x-nonce: ${NONCE}" \
-H "x-signature: ${SIGNATURE}" \
  // ⚠️ IMPORTANT:
  // - inputImage: 1 file or URL (send either file or URL, not both)

  // Option 1: Send inputImage as FILE
  -F "inputImage=@path/to/image.jpg" \
  -F "inputImageUrl=" \

  // Option 2: Send inputImage as URL
  // -F "inputImage=" \
  // -F "inputImageUrl=https://cdn.wiro.ai/uploads/sampleinputs/input-canny-controlnet.jpg" \
  -F "preprocess=--pre_process" \
  -F "prompt=<ct:canny:controlnet-canny-sdxl-1.0:1.0>, a blue paradise bird in the jungle, realistic, 8k, real" \
  -F "negativePrompt=blurry, text, watermark, painted illustration, sketch" \
  -F "samples=4" \
  -F "steps=30" \
  -F "scale=7" \
  -F "seed=7090336" \
  -F "width=1024" \
  -F "height=1024" \
  -F "scheduler=EulerDiscreteScheduler" \
  -F "callbackUrl=Optional: Webhook URL for task completion notifications";

    
                        

Run Command - Response

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

Get Task Detail - Make HTTP Post Request with Task Token

                          
curl -X POST "https://api.wiro.ai/v1/Task/Detail"  \
-H "Content-Type: application/json" \
-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": "534574",
          "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",
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          "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
}
    
                        

Kill Task - Make HTTP Post Request with Task ID

                          
curl -X POST "https://api.wiro.ai/v1/Task/Kill"  \
-H "Content-Type: application/json" \
-H "x-api-key: ${YOUR_API_KEY}" \
-H "x-nonce: ${NONCE}" \
-H "x-signature: ${SIGNATURE}" \
-d '{
  "taskid": "534574"
}';

    
                        

Kill Task - Response

                          
//response body
{
  "errors": [],
  "tasklist": [
      {
          "id": "534574",
          "uuid": "15bce51f-442f-4f44-a71d-13c6374a62bd",
          "name": "",
          "socketaccesstoken": "ZpYote30on42O4jjHXNiKmrWAZqbRE",
          "parameters": {
              "inputImage": "https://api.wiro.ai/v1/File/mCmUXgZLG1FNjjjwmbtPFr2LVJA112/inputImage-6060136.png"
          },
          "debugoutput": "",
          "debugerror": "",
          "starttime": "1734513809",
          "endtime": "1734513813",
          "elapsedseconds": "6.0000",
          "status": "task_cancel",
          "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
}
    
                        

Cancel Task - Make HTTP Post Request (For tasks on queue)

                          
curl -X POST "https://api.wiro.ai/v1/Task/Cancel"  \
-H "Content-Type: application/json" \
-H "x-api-key: ${YOUR_API_KEY}" \
-H "x-nonce: ${NONCE}" \
-H "x-signature: ${SIGNATURE}" \
-d '{
  "taskid": "634574"
}';

    
                        

Cancel Task - Response

                          
//response body
{
  "errors": [],
  "tasklist": [
      {
          "id": "634574",
          "uuid": "15bce51f-442f-4f44-a71d-13c6374a62bd",
          "name": "",
          "socketaccesstoken": "ZpYote30on42O4jjHXNiKmrWAZqbRE",
          "parameters": {
              "inputImage": "https://api.wiro.ai/v1/File/mCmUXgZLG1FNjjjwmbtPFr2LVJA112/inputImage-6060136.png"
          },
          "debugoutput": "",
          "debugerror": "",
          "starttime": "1734513809",
          "endtime": "1734513813",
          "elapsedseconds": "6.0000",
          "status": "task_cancel",
          "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

Preprocess type for ControlNet

Tell us about any details you want to generate

controlnet-canny-sdxl-1.0 scale:

Specify things to not see in the output

1700758180 Report This Model





SDXL-controlnet: Canny


These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. You can find some example images in the following.
prompt: a couple watching a romantic sunset, 4k photo

prompt: ultrarealistic shot of a furry blue bird

prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot

prompt: Cinematic, neoclassical table in the living room, cinematic, contour, lighting, highly detailed, winter, golden hour

prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors.






Usage


Make sure to first install the libraries:
pip install accelerate transformers safetensors opencv-python diffusers

And then we're ready to go:
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2

prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = 'low quality, bad quality, sketches'

image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")

controlnet_conditioning_scale = 0.5 # recommended for good generalization

controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()

image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)

images = pipe(
prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
).images

images[0].save(f"hug_lab.png")


To more details, check out the official documentation of StableDiffusionXLControlNetPipeline.





Training


Our training script was built on top of the official training script that we provide here.





Training data


This checkpoint was first trained for 20,000 steps on laion 6a resized to a max minimum dimension of 384.
It was then further trained for 20,000 steps on laion 6a resized to a max minimum dimension of 1024 and
then filtered to contain only minimum 1024 images. We found the further high resolution finetuning was
necessary for image quality.





Compute


one 8xA100 machine





Batch size


Data parallel with a single gpu batch size of 8 for a total batch size of 64.





Hyper Parameters


Constant learning rate of 1e-4 scaled by batch size for total learning rate of 64e-4





Mixed precision


fp16

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