Platform For AI:Quickly deploy a Stable Diffusion API service in EAS

Send asynchronous requests and subscribe to the results

1. Obtain the invocation information.

   On the EAS-Online Model Services page, find the service that you deployed and click Invocation Method in the Service Type column. In the Invocation Method dialog box, click the Asynchronous Invocation tab. On the Request URL (Internet) tab, obtain the endpoint and token of the service.

2. Send asynchronous requests.

   Note

   • The queue service requires that the size of input queues or output queues cannot exceed 8 KB. Take note of the following items:

     • If the request data contains an image, we recommend that you use a URL to pass the image data. SD WebUI automatically downloads and parses the image data.

     • To ensure that the response does not contain any original image data, we recommend that you use the save_dir parameter to specify the path where the generated image is saved. For more information, see the "Additional request parameters provided EAS" section of this topic.

   • By default, EAS cannot access the Internet. If you set the image_link parameter to an Internet URL, you must complete the required configurations so that EAS can access the image. For more information, see Configure network connectivity and Configure Internet access and a whitelist.

   Use SDK for Python

   Sample code:

   import requests
   
   url = "<service_url>"
   session = requests.session()
   session.headers.update({"Authorization": "<token>"})
   
   prompts = ["cute dog", "cute cat", "cute girl"]
   
   for i in range(5):
       p = prompts[i % len(prompts)]
       payload = {
           "prompt": p,
           "steps": 20,
           "alwayson_scripts": {
               "save_dir": "/code/stable-diffusion-webui/data-oss/outputs/txt2img"
           },
       }
       response = session.post(url=f'{url}/sdapi/v1/txt2img?task_id=txt2img_{i}', json=payload)
       if response.status_code != 200:
           exit(f"send request error:{response.content}")
       else:
           print(f"send {p} success, index is {response.content}")
   
   
   for i in range(5):
       p = prompts[i % len(prompts)]
       payload = {
           "prompt": p,
           "steps": 20,
           "alwayson_scripts": {
               "save_dir": "/code/stable-diffusion-webui/data-oss/outputs/img2img",
               "image_link": "https://eas-cache-cn-hangzhou.oss-cn-hangzhou-internal.aliyuncs.com/stable-diffusion-cache/tests/boy.png",
           },
       }
       response = session.post(url=f'{url}/sdapi/v1/img2img?task_id=img2img_{i}', json=payload)
       if response.status_code != 200:
           exit(f"send request error:{response.content}")
       else:
           print(f"send {p} success, index is {response.content}")
   

   Modify the following parameters in the preceding code:

   • <service_url>: Set this parameter to the endpoint that you obtained in Step 1.

   • <token>: Set this parameter to the token that you obtained in Step 1.

   Note

   • You can use SDK for Python to send POST requests to the API endpoints provided by SD WebUI. Select an endpoint based on your business requirements.

   • If you want to pass custom information to the service, specify a custom tag by using a URL parameter. For example, you can append ?task_id=task_abc to the request path to specify a tag named task_id. The tag information is included in the result within the tags parameter.

   If the code runs successfully, the following result is returned. Your actual result may vary.

   Use SDK for Java

   Refer to the following code to send asynchronous requests. Since Maven is commonly used to manage Java projects, you must add EAS SDK for Java as a dependency in the pom.xml file. For more information, see SDK for Java.

   import com.aliyun.openservices.eas.predict.http.HttpConfig;
   import com.aliyun.openservices.eas.predict.http.QueueClient;
   import com.aliyun.openservices.eas.predict.queue_client.QueueUser;
   import org.apache.commons.lang3.tuple.Pair;
   
   import java.util.HashMap;
   
   public class SDWebuiAsyncPutTest {
       public static void main(String[] args) throws Exception {
           // Create a client for the queue service. 
           String queueEndpoint = "http://166233998075****.cn-hangzhou.pai-eas.aliyuncs.com";
           String queueToken = "xxxxx==";
           // The name of the input queue consists of the service name and the request path that you want to use. 
           String inputQueueName = "<service_name>/sdapi/v1/txt2img";
   
           // Create the input queue. After request data is added to the input queue, the inference service automatically reads the request data from the input queue. 
           QueueClient inputQueue =
               new QueueClient(queueEndpoint, inputQueueName, queueToken, new HttpConfig(), new QueueUser());
           // Clear queue data. Proceed with caution. 
           // input_queue.clear();
   
           // Add request data to the input queue. 
           int count = 5;
           for (int i = 0; i < count; ++i) {
               // Create request data. 
               String data = "{\n" +
                   "    \"prompt\": \"cute dog\", \n" +
                   "    \"steps\":20,\n" +
                   "    \"alwayson_scripts\":{\n" +
                   "        \"save_dir\":\"/code/stable-diffusion-webui/data-oss/outputs/txt2img\"\n" +
                   "    }\n" +
                   "  }";
               // Create a custom tag. 
               HashMap<String, String> map = new HashMap<String, String>(1);
               map.put("task_id", "txt2img_" + i);
               Pair<Long, String> entry = inputQueue.put(data.getBytes(), map);
   
               System.out.println(String.format("send success, index is %d, request_id is %s", entry.getKey(), entry.getValue()));
               // The queue service supports multi-priority queues. You can use the put function to set the priority level of the request. The default value is 0. A value of 1 specifies a high priority. 
               //  inputQueue.put(data.getBytes(), 0L, null);
           }
           // Close the client. 
           inputQueue.shutdown();
   
   
           inputQueueName = "<service_name>/sdapi/v1/img2img";
           inputQueue =
               new QueueClient(queueEndpoint, inputQueueName, queueToken, new HttpConfig(), new QueueUser());
           for (int i = 0; i < count; ++i) {
               // Create request data. 
               String data = "{\n" +
                   "    \"prompt\": \"cute dog\", \n" +
                   "    \"steps\":20,\n" +
                   "    \"alwayson_scripts\":{\n" +
                   "        \"save_dir\":\"/code/stable-diffusion-webui/data-oss/outputs/img2img\",\n" +
                   "        \"image_link\":\"https://eas-cache-cn-hangzhou.oss-cn-hangzhou-internal.aliyuncs.com/stable-diffusion-cache/tests/boy.png\"\n" +
                   "    }\n" +
                   "  }";
               HashMap<String, String> map = new HashMap<String, String>(1);
               map.put("task_id", "img2img_" + i);
               Pair<Long, String> entry = inputQueue.put(data.getBytes(), map);
   
               System.out.println(String.format("send success, index is %d, requestId is %s", entry.getKey(), entry.getValue()));
           }
   
           // Close the client. 
           inputQueue.shutdown();
       }
   }

   Modify the following parameters in the preceding code:

   • queueEndpoint: Set this parameter to the endpoint that you obtained in Step 1. You can refer to the sample code to configure this parameter.

   • queueToken: Set this parameter to the token that you obtained in Step 1.

   • <service_name>: Set this parameter to the name of the asynchronous service that you deployed.

   Note

   If you want to pass custom information to the service, specify a custom tag in the put function. You can refer to the sample code to configure custom tags. The tag information is included in the result within the tags parameter.

   If the code runs successfully, the following result is returned. Your actual result may vary.

   send success, index is 21, request_id is 05ca7786-c24e-4645-8538-83d235e791fe
   send success, index is 22, request_id is 639b257a-7902-448d-afd5-f2641ab77025
   send success, index is 23, request_id is d6b2e127-eba3-4414-8e6c-c3690e0a487c
   send success, index is 24, request_id is 8becf191-962d-4177-8a11-7e4a450e36a7
   send success, index is 25, request_id is 862b2d8e-5499-4476-b3a5-943d18614fc5
   send success, index is 26, requestId is 9774a4ff-f4c8-40b7-ba43-0b1c1d3241b0
   send success, index is 27, requestId is fa536d7a-7799-43f1-947f-71973bf7b221
   send success, index is 28, requestId is e69bdd32-5c7b-4c8f-ba3e-e69d2054bf65
   send success, index is 29, requestId is c138bd8f-be45-4a47-a330-745fd1569534
   send success, index is 30, requestId is c583d4f8-8558-4c8d-95f7-9c3981494007
   
   Process finished with exit code 0

3. Subscribe to the results of the asynchronous requests.

   Use SDK for Python

   Sample code:

   import json
   import oss2
   
   from eas_prediction import QueueClient
   
   sink_queue = QueueClient('139699392458****.cn-hangzhou.pai-eas.aliyuncs.com', 'sd_async/sink')
   sink_queue.set_token('<token>')
   sink_queue.init()
   
   mount_path = "/code/stable-diffusion-webui/data-oss"
   oss_url = "oss://<examplebucket>/aohai-singapore"
   # Do not use the AccessKey pair of an Alibaba Cloud account as it grants permissions to all API operations and poses a high risk. We recommend that you use the AccessKey pair of a Resource Access Management (RAM) user for API operations and O&M. To create a RAM user, log on to the RAM console. 
   auth = oss2.Auth('<yourAccessKeyId>', '<yourAccessKeySecret>')
   # Specify the endpoint and examplebucket parameters based on your business requirements. 
   bucket = oss2.Bucket(auth, 'http://oss-cn-hangzhou.aliyuncs.com', '<examplebucket>')
   
   
   watcher = sink_queue.watch(0, 5, auto_commit=False)
   for x in watcher.run():
       if 'task_id' in x.tags:
           print('index {} task_id is {}'.format(x.index, x.tags['task_id']))
       print(f'index {x.index} data is {x.data}')
       sink_queue.commit(x.index)
       try:
           data = json.loads(x.data.decode('utf-8'))
           for idx, path in enumerate(data['parameters']['image_url'].split(',')):
               url = path.replace(mount_path, oss_url)
               # Download the OSS object to the local file system. 
               bucket.get_object_to_file(url[len("oss://<examplebucket>/"):], f'{x.index}-output-{idx}.png')
               print(f'save {url} to {x.index}-output-{idx}.png')
       except Exception as e:
           print(f'index {x.index} process data error {e}')
   

   Modify the following parameters in the preceding code:

   • sink_queue: Update the service endpoint and service name that are specified in this parameter. For more information about how to obtain the service endpoint, see the "Obtain the invocation information" section of this topic. You can refer to the sample code to configure this parameter.

   • <token>: Set this parameter to the token that you obtained in Step 1.

   • oss_url: Set this parameter to the OSS path that you specified when you deployed the service.

   • <examplebucket>: Set this parameter to the name of the OSS bucket.

   • bucket: This example uses the China (Hangzhou) region. Configure the parameter based on your actual situation. For more information, see Regions and endpoints.

   Note

   • You can manually commit the data or set the auto_commit parameter to true to automatically commit the data.

   • If the client of the queue service stops consuming data, we recommend that you close the client to release resources.

   • You can also run a cURL command or call an API operation to subscribe to the results. For more information, see Asynchronous inference and queue service.

   If the code runs successfully, the following result is returned. Your actual results may vary. You can go to the OSS console and view the generated images in the OSS path that you specified when you deployed the service.

   index 1 task_id is txt2img_0
   index 1 data is b'{"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-16/ad2e270b-dfcc-4ec3-8718-f19824857834-919429454.png","seed":"919429454","error_msg":"","total_time":88.55384421348572},"info":""}'
   save oss://<examplebucket>/aohai-singapore/outputs/txt2img/txt2img-images/2023-08-16/ad2e270b-dfcc-4ec3-8718-f19824857834-919429454.png to 1-output-0.png
   index 2 task_id is txt2img_1
   index 2 data is b'{"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-16/374ceae2-deb5-4448-b213-cfdb11a53892-3273662200.png","seed":"3273662200","error_msg":"","total_time":2.76196551322937},"info":""}'
   save oss://<examplebucket>/aohai-singapore/outputs/txt2img/txt2img-images/2023-08-16/374ceae2-deb5-4448-b213-cfdb11a53892-3273662200.png to 2-output-0.png
   index 3 task_id is txt2img_2
   index 3 data is b'{"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-16/a37a0384-2ed4-42f9-9166-fe0fd80c624c-210444956.png","seed":"210444956","error_msg":"","total_time":2.7492804527282715},"info":""}'
   save oss://<examplebucket>/aohai-singapore/outputs/txt2img/txt2img-images/2023-08-16/a37a0384-2ed4-42f9-9166-fe0fd80c624c-210444956.png to 3-output-0.png
   index 4 task_id is txt2img_3
   index 4 data is b'{"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-16/482dcc64-4848-48f8-b9fd-7f0d3992f1ae-198191449.png","seed":"198191449","error_msg":"","total_time":2.8161191940307617},"info":""}'
   save oss://<examplebucket>/aohai-singapore/outputs/txt2img/txt2img-images/2023-08-16/482dcc64-4848-48f8-b9fd-7f0d3992f1ae-198191449.png to 4-output-0.png

   Use SDK for Java

   Sample code:

   import com.aliyun.openservices.eas.predict.http.HttpConfig;
   import com.aliyun.openservices.eas.predict.http.QueueClient;
   import com.aliyun.openservices.eas.predict.queue_client.DataFrame;
   import com.aliyun.openservices.eas.predict.queue_client.QueueUser;
   import com.aliyun.openservices.eas.predict.queue_client.WebSocketWatcher;
   
   public class SDWebuiAsyncWatchTest {
       public static void main(String[] args) throws Exception {
           // Create a client for the queue service. 
           String queueEndpoint = "http://166233998075****.cn-hangzhou.pai-eas.aliyuncs.com";
           String queueToken = "xxxxx==";
           // The name of the output queue consists of the service name and "/sink". 
           String sinkQueueName = "<service_name>/sink";
   
           // The output queue. The inference service processes the input data and writes the results to the output queue. 
           QueueClient sinkQueue =
               new QueueClient(queueEndpoint, sinkQueueName, queueToken, new HttpConfig(), new QueueUser());
   
           // Clear queue data. Proceed with caution. 
           //  sinkQueue.clear();
   
           // Subscribe to the queue and obtain the results. 
           WebSocketWatcher watcher = sinkQueue.watch(0L, 5L, false, false, null);
           try {
               while (true) {
                   DataFrame df = watcher.getDataFrame();
                   if (df.getTags().containsKey("task_id")) {
                       System.out.println(String.format("task_id = %s", df.getTags().get("task_id")));
                   }
                   System.out.println(String.format("index = %d, data = %s, requestId = %s", df.getIndex(), new String(df.getData()), df.getTags().get("requestId")));
                   sinkQueue.commit(df.getIndex());
               }
           } catch (Exception e) {
               System.out.println("watch error:" + e.getMessage());
               e.printStackTrace();
               watcher.close();
           }
   
           // Close the client. 
           sinkQueue.shutdown();
       }
   }

   Modify the following parameters in the preceding code:

   • queueEndpoint: Set this parameter to the endpoint that you obtained in Step 1. You can refer to the sample code to configure this parameter.

   • queueToken: Set this parameter to the token that you obtained in Step 1.

   • <service_name>: Set this parameter to the name of the asynchronous service that you deployed.

   Note

   • You can manually commit the data or set the auto_commit parameter to true to automatically commit the data.

   • If the client of the queue service stops consuming data, we recommend that you close the client to release resources.

   • You can also run a cURL command or call an API operation to subscribe to the results. For more information, see Asynchronous inference and queue service.

   If the code runs successfully, the following result is returned. Your actual results may vary. You can go to the OSS console and view the generated image in the OSS path that you specified when you deployed the service.

   2023-08-04 16:17:31,497 INFO [com.aliyun.openservices.eas.predict.queue_client.WebSocketWatcher] - WebSocketClient Successfully Connects to Server: 1396993924585947.cn-hangzhou.pai-eas.aliyuncs.com/116.62.4.210:80
   task_id = txt2img_0
   index = 21, data = {"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-04/54363a9d-24a5-41b5-b038-2257d43b8e79-412510031.png","seed":"412510031","error_msg":"","total_time":2.5351321697235107},"info":""}, requestId = 05ca7786-c24e-4645-8538-83d235e791fe
   task_id = txt2img_1
   index = 22, data = {"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-04/0c646dda-4a53-43f4-97fd-1f507599f6ae-2287341785.png","seed":"2287341785","error_msg":"","total_time":2.6269655227661133},"info":""}, requestId = 639b257a-7902-448d-afd5-f2641ab77025
   task_id = txt2img_2
   index = 23, data = {"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/data-oss/outputs/txt2img/txt2img-images/2023-08-04/4d542f25-b9cc-4548-9db2-5addd0366d32-1158414078.png","seed":"1158414078","error_msg":"","total_time":2.6604185104370117},"info":""}, requestId = d6b2e127-eba3-4414-8e6c-c3690e0a487c
   task_id = txt2img_3

Send a synchronous request

After you deploy an asynchronous service, you can still send synchronous requests to the service. You can use a cURL command or a Python client to send a synchronous request to verify the model performance and service availability. Perform the following steps:

1. Obtain the invocation information.

   On the EAS-Online Model Services page, find the service that you deployed and click Invocation Method in the Service Type column. In the Invocation Method dialog box, click the Synchronous Invocation tab. On the Public Endpoint tab, you can view the endpoint and token of the service.

2. Use a cURL command to send a request. Sample code:

   curl -v --location '<service_url>/sdapi/v1/sd-models' \
   --header 'Authorization: <service_token>' 

   Modify the following parameters in the preceding code:

   • <service_url>: Set this parameter to the endpoint that you obtained in Step 1.

   • <token>: Set this parameter to the token that you obtained in Step 1.

   If the command runs successfully, the list of supported models is returned.

Example: txt2img

Sample request body:

{
      "alwayson_scripts": {
          "sd_model_checkpoint": "deliberate_v2.safetensors",  
          "save_dir": "/code/stable-diffusion-webui/data-oss/outputs",
          "sd_vae": "Automatic"
      },
      "steps": 20,
      "prompt": "girls",          
      "batch_size": 1,                                            
      "n_iter": 2,                                                 
      "width": 576, 
      "height": 576,
      "negative_prompt": "ugly, out of frame"
  }

Parameters:

• sd_model_checkpoint: the SD model that you want to use. The SD model can automatically switch to a foundation model.

• sd_vae: the VAE model that you want to use.

• save_dir: the path to save the generated images.

Sample synchronous request:

# Send a synchronous request to verify the model performance. 

curl --location --request POST '<service_url>/sdapi/v1/txt2img' \
--header 'Authorization: <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
      "alwayson_scripts": {
          "sd_model_checkpoint": "deliberate_v2.safetensors",
          "save_dir": "/code/stable-diffusion-webui/data-oss/outputs",
          "sd_vae": "Automatic"
      },
      "prompt": "girls",          
      "batch_size": 1,                                            
      "n_iter": 2,                                                 
      "width": 576, 
      "height": 576,
      "negative_prompt": "ugly, out of frame"
  }'

Sample response to a synchronous request:

{
  "images": [],
  "parameters": {
    "id_task": "14837",
    "status": 0,
    "image_url": "/code/stable-diffusion-webui/data-oss/outputs/txt2img-grids/2023-07-24/grid-29a67c1c-099a-4d00-8ff3-1ebe6e64931a.png,/code/stable-diffusion-webui/data-oss/outputs/txt2img-images/2023-07-24/74626268-6c81-45ff-90b7-faba579dc309-1146644551.png,/code/stable-diffusion-webui/data-oss/outputs/txt2img-images/2023-07-24/6a233060-e197-4169-86ab-1c18adf04e3f-1146644552.png",
    "seed": "1146644551,1146644552",
    "error_msg": "",
    "total_time": 32.22393465042114
  },
  "info": ""
}

Sample asynchronous request:

# Send the request data to the input queue. 
curl --location --request POST '<service_url>/sdapi/v1/txt2img' \
--header 'Authorization: <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
    "alwayson_scripts": {
        "sd_model_checkpoint": "deliberate_v2.safetensors",
        "id_task": "14837",
        "uid": "123",
        "save_dir": "tmp/outputs"
    },
    "prompt": "girls",
    "batch_size": 1,
    "n_iter": 2,
    "width": 576,
    "height": 576,
    "negative_prompt": "ugly, out of frame"
}'

Example: img2img

Sample request body:

{
    "alwayson_scripts": {
        "image_link":"https://eas-cache-cn-hangzhou.oss-cn-hangzhou-internal.aliyuncs.com/stable-diffusion-cache/tests/boy.png",
        "sd_model_checkpoint": "deliberate_v2.safetensors",
        "sd_vae": "Automatic",
        "save_dir": "/code/stable-diffusion-webui/data-oss/outputs"
    },
    "prompt": "girl",
    "batch_size": 1,                                            
    "n_iter": 2,                                                 
    "width": 576, 
    "height": 576,
    "negative_prompt": "ugly, out of frame",
    "steps": 20, # Sampling steps
    "seed": 111,   
    "subseed": 111, # Variation seed
    "subseed_strength": 0, # Variation strength
    "seed_resize_from_h": 0, # Resize seed from height
    "seed_resize_from_w": 0, # Resize seed from width
    "seed_enable_extras": false, # Extra
    "sampler_name": "DDIM", # Sampling method
    "cfg_scale": 7.5, # CFG Scale
    "restore_faces": true, # Restore faces
    "tiling": false, # Tiling
    "init_images": [], # image base64 str, default None
    "mask_blur": 4, # Mask blur
    "resize_mode": 1, # 0 just resize, 1 crop and resize, 2 resize and fill, 3 just resize
    "denoising_strength": 0.75, # Denoising strength
    "inpainting_mask_invert": 0, #int, index of ['Inpaint masked', 'Inpaint not masked'], Mask mode
    "inpainting_fill": 0, #index of ['fill', 'original', 'latent noise', 'latent nothing'], Masked content
    "inpaint_full_res": 0, # index of ["Whole picture", "Only masked"], Inpaint area
    "inpaint_full_res_padding": 32, #minimum=0, maximum=256, step=4, value=32, Only masked padding, pixels
    #"image_cfg_scale": 1, # resized by scale
    #"script_name": "Outpainting mk2", # The name of the script. Skip this field if you do not use a script.
    #"script_args": ["Outpainting", 128, 8, ["left", "right", "up", "down"], 1, 0.05] # The parameters of the script in the following order: fixed fields, pixels, mask_blur, direction, noise_q, and color_variation.
}

Sample response:

{
    "images":[],
    "parameters":{
        "id_task":"14837",
        "status":0,
        "image_url":"/data/api_test/img2img-grids/2023-06-05/grid-0000.png,/data/api_test/img2img-images/2023-06-05/00000-1003.png,/data/api_test/img2img-images/2023-06-05/00001-1004.png",
        "seed":"1003,1004",
        "error_msg":""
    },
    "info":""
}

Example: txt2img with ControlNet

Sample request body:

{
    "alwayson_scripts": {
        "sd_model_checkpoint": "deliberate_v2.safetensors", # The name of the model. 
        "save_dir": "/code/stable-diffusion-webui/data-oss/outputs",
        "controlnet":{
            "args":[
                {
                    "image_link": "https://pai-aigc-dataset.oss-cn-hangzhou.aliyuncs.com/pixabay_images/00008b87bf3ff6742b8cf81c358b9dbc.jpg",
                    "enabled": true, 
                    "module": "canny", 
                    "model": "control_v11p_sd15_canny", 
                    "weight": 1, 
                    "resize_mode": "Crop and Resize", 
                    "low_vram": false, 
                    "processor_res": 512, 
                    "threshold_a": 100, 
                    "threshold_b": 200, 
                    "guidance_start": 0, 
                    "guidance_end": 1, 
                    "pixel_perfect": true, 
                    "control_mode": "Balanced", 
                    "input_mode": "simple", 
                    "batch_images": "", 
                    "output_dir": "", 
                    "loopback": false
                }
            ]
        }
    },
    # Key parameters.
    "prompt": "girls",          
    "batch_size": 1,                                            
    "n_iter": 2,                                                 
    "width": 576, 
    "height": 576,
    "negative_prompt": "ugly, out of frame"
}

Sample response:

{
    "images":[],
    "parameters":{
        "id_task":"14837",
        "status":0,
        "image_url":"/data/api_test/txt2img-grids/2023-06-05/grid-0007.png,/data/api_test/txt2img-images/2023-06-05/00014-1003.png,/data/api_test/txt2img-images/2023-06-05/00015-1004.png",
        "seed":"1003,1004",
        "error_msg":"",
        "image_mask_url":"/data/api_test/controlnet_mask/2023-06-05/00000.png,/data/api_test/controlnet_mask/2023-06-05/00001.png"
    },
    "info":""
}