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Stable Diffusion AWS extension API v2023-07-05

python
Python

javascript
Javascript

python

javascript

Stable Diffusion AWS extension API

Overview

This document describe all the api for Stable Diffusion AWS extension solution. This Solution contains two parts, one part is stable diffusion WebUI extension which is gradio based client to provide a user-friendly interface, another part is called middle-ware which is resources deploy on AWS cloud, the middleware provide several API interfaces to let stable diffusion aws extension client to interact services on AWS cloud like Sagemaker and S3 to do the model update/training and inference operations.

In order to support users who do not use stable diffusion aws extension. We provide this document to list all the API interfaces to help user understand how to call API methods to do the training or inference.

After user deployed solution middleware cloudformation, user can get the API URL and Token in the output part of the main stack. Please refer to this document

Base URLs:

https://{api_id}.execute-api.{region}.amazonaws.com/prod

Authentication

API Key (api_key)
- Parameter Name: x-api-key, in: header.
Authorization
- Use Bearer Token in headers

User Scenarios

1. Deploy a new Endpoint

Call /endpoints to create a new sagemaker endpoint, you need to specify two parameters for creating, one is instance_type, candidate values are "ml.g4dn.2xlarge","ml.g4dn.4xlarge","ml.g4dn.8xlarge","ml.g4dn.12xlarge", another is initial_instance_count, candidate values are 1|2|3|4|5|6.

After calling /endpoints, you need to call /endpoints to list all the endpoint status. Normally it took about more than 10 minutes to make a new Sagemaker endpoint change to InService status. The Sagemaker endpoint can only be used for inference when it is InService status.

If the endpoint is in failed status, you can call /inference/get-endpoint-deployment-job with parameter jobID, the response will show the reason why endpoint deployment is failed, normally it is caused by AWS account quota limitation.

sequence digram raw

title Create a Sagemaker Endpoint

Client->Middleware:Call /endpoints Middleware->Middleware: Start a workflow to configure sagemaker endpoint \n based on uer request configuration Client->Middleware:Call /endpoints \n to list all the endpoint creation job list Client->Middleware:Call /inference/get-endpoint-deployment-job \n to check whether Sagemaker endpoint is in \n 'InService' state.

2. Upload a model

3. Train a model

4. Do Inference

After Sagemaker endpoint is in InService status, you can call /inference/v2 to do the txt2image or image2image inference. You specify the endpoint name in "sagemaker_endpoint" parameter in the post body of the request. Other required parameters are located in /inference/v2.

/inference/v2 will return following json structure to client:

{
  "inference_id": "XXXXXXX",
  "status": "inprogress | failed",
  "endpoint_name": "NAME_OF_ENDPOINT",
  "output_path": "path_of_prediction_output"
}

Client then can call /inference/get-inference-job using the inference_id as parameter to query the inference job status. If the inference job has finished successfully(status is "succeed"), Client can use /inference/get-inference-job-image-output to get all inference result images, the images will be returned as S3 presigned url list so client can download. following is am example of get-inference-job-image-output result:

[
  "https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/out/1f9679f3-25b8-4c44-8345-0a845da30094/result/image_0.jpg"
]

Also Client can call /inference/get-inference-job-param-output to get all the inference parameters, the response of /inference/get-inference-job-param-output is an S3 presigned url contains the json format of the parameters, following is an response example:

[
  "https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/out/1f9679f3-25b8-4c44-8345-0a845da30094/result/1f9679f3-25b8-4c44-8345-0a845da30094_param.json"
]

sequence digram raw

title Do Inference

Client->Middleware:Call /inference/v2 Middleware->Middleware: Start an async inference job \n on configure sagemaker endpoint \n based on uer request configuration Middleware->Client: return inference_id Client->Middleware:Call /inference/get-inference-job \n to query the inference job status Middleware->Client: return inference_id and the job status(inprocess | succeed | failure)

abox over Client: If the inference job is succeed, \n call /inference/get-inference-job-image-output and \n /inference/get-inference-job-param-output to get the \n inference result Client->Middleware:Call /inference/get-inference-job-image-output \n to get all inference result images.

Middleware->Client: return the inference result images in presigned url format

Client->Middleware:Call /inference/get-inference-job-param-output \n to get inference parameters.

Middleware->Client: return the inference parameter in presigned url format

# API List

Index	Http Method	API Name	Description
1	GET	/inference/test-connection	Test whether client can connect to api and check the API_TOKEN is correct
3	GET	/inference/get-inference-job	Retrieves details of a specific inference job.
4	GET	/inference/get-inference-job-image-output	Gets image output of a specific inference job.
5	GET	/inference/get-inference-job-param-output	Gets parameter output of a specific inference job.
6	POST	/inference/v2	Run sagemaker inference using default parameters
7	POST	/endpoints	Deploys a SageMaker endpoint.
8	DELETE	/endpoints	Deletes a SageMaker endpoint.
9	GET	/endpoints	Lists all endpoint deployment jobs.
10	GET	/inference/get-endpoint-deployment-job	Gets a specific endpoint deployment job.
11	GET	/inference/generate-s3-presigned-url-for-uploading	Generates an S3 presigned URL for uploading.
12	GET	/inference/get-texual-inversion-list	Gets the list of textual inversions.
13	GET	/inference/get-lora-list	Gets the list of LoRa.
14	GET	/inference/get-hypernetwork-list	Gets the list of hypernetworks.
15	GET	/inference/get-controlnet-model-list	Gets the list of ControlNet models.
16	POST	/inference/run-model-merge	Runs a model merge.
17	POST	/model	Creates a new model.
18	PUT	/model	Upload the model file
19	GET	/models	Lists all models.
20	GET	/checkpoint	Gets a checkpoint.
21	PUT	/checkpoint	Updates a checkpoint.
22	GET	/checkpoints	Lists all checkpoints.
23	PUT	/inference/v2/{id}/run	Starts a training job.
24	PUT	/train	Updates a training job.
25	GET	/trains	Lists all training jobs.
26	POST	/dataset	Creates a new dataset.
27	PUT	/dataset	Updates a dataset.
28	GET	/datasets	Lists all datasets.
29	GET	/{dataset_name}/data	Gets data of a specific dataset.
30	POST	/upload_checkpoint	Upload directly to s3 according to the model url.

/inference/test-connection

test middleware connection

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/test-connection', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/test-connection',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/test-connection

Example responses

200 Response

{
    "message": "Success"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

/inference/v2

Generate a new image from a text prompt.

Code samples :

Python example code:

import requests
headers = {
  "Content-Type": "application/json",
  "Accept": "application/json",
  'x-api-key': 'API_TOKEN_VALUE'
}
body = {
  "task_type": "txt2img",
  "models": {
    "Stable-diffusion": [
      "v1-5-pruned-emaonly.safetensors"
    ]
  },
  "sagemaker_endpoint_name": "infer-endpoint-cb821ea",
  "prompt": "a cute panda",
  "denoising_strength": 0.75
}

r = requests.post("https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/v2", headers = headers, json = body)

print(r.json())

Javascript example code:

const inputBody = '{
  "task_type": "txt2img",
  "models": {
    "Stable-diffusion": [
      "v1-5-pruned-emaonly.safetensors"
    ]
  },
  "sagemaker_endpoint_name": "infer-endpoint-cb821ea",
  "prompt": "a cute panda",
  "denoising_strength": 0.75
}';
const headers = {
  "Content-Type":"application/json",
  "Accept":"application/json",
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch("https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/v2",
{
  method: "POST",
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /inference/v2

Body parameter

{
  "task_type": "txt2img",
  "models": {
    "Stable-diffusion": [
      "v1-5-pruned-emaonly.safetensors"
    ]
  },
  "sagemaker_endpoint_name": "infer-endpoint-cb821ea",
  "prompt": "a cute panda",
  "denoising_strength": 0.75
}

Parameters

Parameter example

Below JSON shows the parameters with default value. sagemaker_endpoint_name, task_type, prompt and Stable-diffusion are mandatory, other parameters are optional.

{
  "sagemaker_endpoint_name": "infer-endpoint-ef3abcd", 
  "task_type": "txt2img",
  "prompt": "",
  "models": {
    "Stable-diffusion": [
      "v1-5-pruned-emaonly.safetensors"
    ],
    "Lora": [
      "raidenshogun1-000009.safetensors"
    ]
  },
  "enable_hr": false,
  "denoising_strength": null,
  "firstphase_width": 0,
  "firstphase_height": 0,
  "hr_scale": 2.0,
  "hr_upscaler": "Latent",
  "hr_second_pass_steps": 0,
  "hr_resize_x": 0,
  "hr_resize_y": 0,
  "hr_sampler_name": null,
  "hr_prompt": "",
  "hr_negative_prompt": "",
  "prompt": "",
  "styles": [],
  "seed": -1,
  "subseed": -1,
  "subseed_strength": 0.0,
  "seed_resize_from_h": 0,
  "seed_resize_from_w": 0,
  "sampler_name": "Euler a",
  "batch_size": 1,
  "n_iter": 1,
  "steps": 28,
  "cfg_scale": 7.0,
  "width": 512,
  "height": 512,
  "restore_faces": false,
  "tiling": false,
  "do_not_save_samples": false,
  "do_not_save_grid": false,
  "negative_prompt": "",
  "eta": null,
  "s_min_uncond": 0.0,
  "s_churn": 0.0,
  "s_tmax": 1.0,
  "s_tmin": 0.0,
  "s_noise": 1.0,
  "override_settings": {},
  "override_settings_restore_afterwards": true,
  "script_args": [],
  "sampler_index": "Euler a",
  "script_name": null,
  "send_images": true,
  "save_images": false,
  "alwayson_scripts": {
    "controlnet": {
      "args": [
        {
          "enabled": false,
          "module": "none",
          "model": "None",
          "weight": 1,
          "image": null,
          "resize_mode": "Crop and Resize",
          "low_vram": false,
          "processor_res": -1,
          "threshold_a": -1,
          "threshold_b": -1,
          "guidance_start": 0,
          "guidance_end": 1,
          "pixel_perfect": false,
          "control_mode": "Balanced",
          "is_ui": true,
          "input_mode": "simple",
          "batch_images": "",
          "output_dir": "",
          "loopback": false
        }
      ]
    },
    "extra options": {
      "args": []
    }
  }
}

Example responses

200 Response

{
  "inference_id": "XXXXXXX",
  "status": "inprogress | failed",
  "endpoint_name": "NAME_OF_ENDPOINT",
  "output_path": "path_of_prediction_output"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

/endpoints(POST)

Create SageMaker Endpoint

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}
inputBody = {
	"instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
	"initial_instance_count": "1|2|3|4|5|6"
}

r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
  "initial_instance_count": "1|2|3|4|5|6"
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /endpoints

Body parameter

{
  "instance_type": "ml.g4dn.xlarge | ml.g4dn.2xlarge | ml.g4dn.4xlarge",
  "initial_instance_count": "1|2|3|4|5|6"
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

null

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/endpoints(DELETE)

Delete Endpoints

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"delete_endpoint_list": [
  "infer-endpoint-XXXXXX",
  "infer-endpoint-YYYYYY"
]
}

r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "delete_endpoint_list": [
    "infer-endpoint-XXXXXX",
    "infer-endpoint-YYYYYY"
  ]
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /endpoints

Body parameter

{
  "delete_endpoint_list": [
    "infer-endpoint-XXXXXX",
    "infer-endpoint-YYYYYY"
  ]
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

Endpoint deleted

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/endpoints(GET)

List Endpoints

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/endpoints',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /endpoints

Example responses

200 Response

[
  {
    "EndpointDeploymentJobId": "e0f9ccfd-8d14-4e77-9e75-b340e1ef23c8",
    "startTime": "2023-07-04 08:00:35.171309",
    "endTime": "2023-07-04 08:00:37.158519",
    "error": "",
    "status": "failed"
  },
  {
    "EndpointDeploymentJobId": "1bd447d2-e561-4cb3-965d-2707b30aea81",
    "startTime": "2023-07-04 08:00:22.435828",
    "endTime": "2023-07-04 08:00:25.421777",
    "error": "",
    "status": "failed"
  },
  {
    "EndpointDeploymentJobId": "cb821ea9-e9d1-4bae-98f8-c20ecadf11e0",
    "startTime": "2023-07-04 08:00:47.736033",
    "endTime": "2023-07-04 08:12:55.148070",
    "endpoint_name": "infer-endpoint-cb821ea",
    "endpoint_status": "InService",
    "status": "success"
  }
]

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/list-inference-jobs

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/list-inference-jobs', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/list-inference-jobs',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/list-inference-jobs

Example responses

200 Response

[
  {
    "inference_info_name": "/tmp/417d8bc5-f6d0-49c6-9669-c981beeb602a_param.json",
    "startTime": "2023-07-04 09:14:21.170303",
    "taskType": "txt2img",
    "completeTime": "2023-07-04-09-14-26",
    "InferenceJobId": "417d8bc5-f6d0-49c6-9669-c981beeb602a",
    "status": "succeed",
    "sagemakerRaw": "",
    "image_names": [
      "image_0.jpg"
    ]
  },
  {
    "inference_info_name": "/tmp/1f9679f3-25b8-4c44-8345-0a845da30094_param.json",
    "startTime": "2023-07-05 06:38:45.752740",
    "taskType": "txt2img",
    "completeTime": "2023-07-05-06-38-49",
    "InferenceJobId": "1f9679f3-25b8-4c44-8345-0a845da30094",
    "status": "succeed",
    "sagemakerRaw": "{'awsRegion': 'us-west-2', 'eventTime': '2023-07-05T06:38:47.73Z', 'receivedTime': '2023-07-05T06:38:45.725Z', 'invocationStatus': 'Completed', 'requestParameters': {'accept': '*/*', 'endpointName': 'infer-endpoint-cb821ea', 'inputLocation': 's3://sagemaker-us-west-2-489670441870/async-endpoint-inputs/infer-endpoint-cb821ea-230705-0638/2023-07-05-06-38-45-445-81a1ec03-f000-4a20-9a60-032ab1558a9d'}, 'responseParameters': {'contentType': 'application/json', 'outputLocation': 's3://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3/sagemaker_output/211d2095-68bc-4404-a9e1-8a18a41f4dc7.out'}, 'inferenceId': '1f9679f3-25b8-4c44-8345-0a845da30094', 'eventVersion': '1.0', 'eventSource': 'aws:sagemaker', 'eventName': 'InferenceResult'}",
    "image_names": [
      "image_0.jpg"
    ]
  }
]

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/get-endpoint-deployment-job

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-endpoint-deployment-job', params={
  'jobID': 'string'
}, headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-endpoint-deployment-job?jobID=string',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-endpoint-deployment-job

Parameters

Name	In	Type	Required	Description
jobID	query	string	true	none

Example responses

200 Response

{
  "EndpointDeploymentJobId": "cb821ea9-e9d1-4bae-98f8-c20ecadf11e0",
  "startTime": "2023-07-04 08:00:47.736033",
  "endTime": "2023-07-04 08:12:55.148070",
  "endpoint_name": "infer-endpoint-cb821ea",
  "endpoint_status": "InService",
  "status": "success"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline
422	Unprocessable Entity	Validation Error	HTTPValidationError

Response Schema

/inference/get-inference-job

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-inference-job

Parameters

Name	In	Type	Required	Description
jobID	query	string	false	none

Example responses

200 Response

{
  "inference_info_name": "/tmp/1f9679f3-25b8-4c44-8345-0a845da30094_param.json",
  "startTime": "2023-07-05 06:38:45.752740",
  "taskType": "txt2img",
  "completeTime": "2023-07-05-06-38-49",
  "InferenceJobId": "1f9679f3-25b8-4c44-8345-0a845da30094",
  "status": "succeed",
  "sagemakerRaw": "{'awsRegion': 'us-west-2', 'eventTime': '2023-07-05T06:38:47.73Z', 'receivedTime': '2023-07-05T06:38:45.725Z', 'invocationStatus': 'Completed', 'requestParameters': {'accept': '*/*', 'endpointName': 'infer-endpoint-cb821ea', 'inputLocation': 's3://sagemaker-us-west-2-489670441870/async-endpoint-inputs/infer-endpoint-cb821ea-230705-0638/2023-07-05-06-38-45-445-81a1ec03-f000-4a20-9a60-032ab1558a9d'}, 'responseParameters': {'contentType': 'application/json', 'outputLocation': 's3://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3/sagemaker_output/211d2095-68bc-4404-a9e1-8a18a41f4dc7.out'}, 'inferenceId': '1f9679f3-25b8-4c44-8345-0a845da30094', 'eventVersion': '1.0', 'eventSource': 'aws:sagemaker', 'eventName': 'InferenceResult'}",
  "image_names": [
    "image_0.jpg"
  ]
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline
422	Unprocessable Entity	Validation Error	HTTPValidationError

Response Schema

/inference/get-inference-job-image-output

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job-image-output', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job-image-output',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-inference-job-image-output

Parameters

Name	In	Type	Required	Description
jobID	query	string	false	none

Example responses

200 Response

[
  "https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/out/1f9679f3-25b8-4c44-8345-0a845da30094/result/image_0.jpg"
]

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline
422	Unprocessable Entity	Validation Error	HTTPValidationError

Response Schema

Status Code 200

Response Get Inference Job Image Output Inference Get Inference Job Image Output Get

Name	Type	Required	Restrictions	Description
Response Get Inference Job Image Output Inference Get Inference Job Image Output Get	[string]	false	none	none

/inference/get-inference-job-param-output

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job-param-output', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-inference-job-param-output',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-inference-job-param-output

Parameters

Name	In	Type	Required	Description
jobID	query	string	false	none

Example responses

200 Response

[
  "https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/out/1f9679f3-25b8-4c44-8345-0a845da30094/result/1f9679f3-25b8-4c44-8345-0a845da30094_param.json"
]

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline
422	Unprocessable Entity	Validation Error	HTTPValidationError

/inference/generate-s3-presigned-url-for-uploading

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/generate-s3-presigned-url-for-uploading', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/generate-s3-presigned-url-for-uploading',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/generate-s3-presigned-url-for-uploading

Parameters

Name	In	Type	Required	Description
s3_bucket_name	query	string	false	none
key	query	string	false	none

Example responses

200 Response

"https://stable-diffusion-aws-extension-aigcbucketa457cb49-1tlr2pqwkosg3.s3.amazonaws.com/config/aigc.json?XXX"

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	string
422	Unprocessable Entity	Validation Error	HTTPValidationError

/inference/get-texual-inversion-list

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-texual-inversion-list', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-texual-inversion-list',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-texual-inversion-list

Example responses

200 Response

null

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/get-lora-list

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-lora-list', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-lora-list',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-lora-list

Example responses

200 Response

null

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/get-hypernetwork-list

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-hypernetwork-list', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-hypernetwork-list',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-hypernetwork-list

Example responses

200 Response

null

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/get-controlnet-model-list

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-controlnet-model-list', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/get-controlnet-model-list',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /inference/get-controlnet-model-list

Example responses

200 Response

null

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

/inference/run-model-merge

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/run-model-merge', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/inference/run-model-merge',
{
  method: 'POST',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /inference/run-model-merge

Example responses

200 Response

{
  "primary_model_name": "primary_model_name,",
  "secondary_model_name": "secondary_model_name,",
  "tertiary_model_name": "teritary_model_name"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful Response	Inline

Response Schema

default

/model(POST)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model', headers = headers)

print(r.json())

Javascript example code:

const inputBody = '{
  "model_type": "Stable-diffusion",
  "name": "testmodelcreation01",
  "filenames": [
    {
      "filename": "v1-5-pruned-emaonly.safetensors.tar",
      "parts_number": 5
    }
  ],
  "params": {
    "create_model_params": {
      "new_model_name": "testmodelcreation01",
      "ckpt_path": "v1-5-pruned-emaonly.safetensors",
      "from_hub": false,
      "new_model_url": "",
      "new_model_token": "",
      "extract_ema": false,
      "train_unfrozen": false,
      "is_512": true
    }
  }
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /model

Body parameter

{
  "model_type": "Stable-diffusion",
  "name": "testmodelcreation01",
  "filenames": [
    {
      "filename": "v1-5-pruned-emaonly.safetensors.tar",
      "parts_number": 5
    }
  ],
  "params": {
    "create_model_params": {
      "new_model_name": "testmodelcreation01",
      "ckpt_path": "v1-5-pruned-emaonly.safetensors",
      "from_hub": false,
      "new_model_url": "",
      "new_model_token": "",
      "extract_ema": false,
      "train_unfrozen": false,
      "is_512": true
    }
  }
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "job": {
    "id": "id",
    "status": "Initialed",
    "s3_base": "s3://S3_Location",
    "model_type": "Stable-diffusion",
    "params": {},
    "s3PresignUrl": [
      {
        "upload_id": "id",
        "bucket": "bucket name",
        "key": "object key"
      }
    ]
  }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/model(PUT)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"model_id": "c9f59ee7-0672-4fd1-8a45-8a494de8a48d",
"status": "Creating",
"multi_parts_tags": {
  "v1-5-pruned-emaonly.safetensors.tar": [
    {
      "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
      "PartNumber": 1
    },
    {
      "ETag": "e4378bd84b0497559c55be8373cb79d0",
      "PartNumber": 2
    },
    {
      "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
      "PartNumber": 3
    },
    {
      "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
      "PartNumber": 4
    },
    {
      "ETag": "e613d37e5065b0cd63f1cad216423141",
      "PartNumber": 5
    }
  ]
}
}

r = requests.put('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "model_id": "c9f59ee7-0672-4fd1-8a45-8a494de8a48d",
  "status": "Creating",
  "multi_parts_tags": {
    "v1-5-pruned-emaonly.safetensors.tar": [
      {
        "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
        "PartNumber": 1
      },
      {
        "ETag": "e4378bd84b0497559c55be8373cb79d0",
        "PartNumber": 2
      },
      {
        "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
        "PartNumber": 3
      },
      {
        "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
        "PartNumber": 4
      },
      {
        "ETag": "e613d37e5065b0cd63f1cad216423141",
        "PartNumber": 5
      }
    ]
  }
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/model',
{
  method: 'PUT',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /model

Body parameter

{
  "model_id": "c9f59ee7-0672-4fd1-8a45-8a494de8a48d",
  "status": "Creating",
  "multi_parts_tags": {
    "v1-5-pruned-emaonly.safetensors.tar": [
      {
        "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
        "PartNumber": 1
      },
      {
        "ETag": "e4378bd84b0497559c55be8373cb79d0",
        "PartNumber": 2
      },
      {
        "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
        "PartNumber": 3
      },
      {
        "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
        "PartNumber": 4
      },
      {
        "ETag": "e613d37e5065b0cd63f1cad216423141",
        "PartNumber": 5
      }
    ]
  }
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "job": {
    "output_path": "s3://S3_Location",
    "id": "job.id",
    "endpointName": "endpoint_name",
    "jobStatus": "Created",
    "jobType": "Stable-diffusion"
  }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/models(GET)

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/models', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/models',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /models

Parameters

Name	In	Type	Required	Description
types	query	array[string]	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "models": {
    "id": "id",
    "model_name": "name",
    "created": 12341234,
    "params": {},
    "status": "Initialed",
    "output_s3_location": "s3://S3_LOCATION/"
  }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/checkpoint

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"checkpoint_type": "Stable-diffusion",
"filenames": [
  {
    "filename": "v1-5-pruned-emaonly.safetensors",
    "parts_number": 5
  }
],
"params": {
  "new_model_name": "test_api",
  "number": 1,
  "string": "abc"
}
}

r = requests.post('https://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/checkpoint', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "checkpoint_type": "Stable-diffusion",
  "filenames": [
    {
      "filename": "v1-5-pruned-emaonly.safetensors",
      "parts_number": 5
    }
  ],
  "params": {
    "new_model_name": "test_api",
    "number": 1,
    "string": "abc"
  }
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/checkpoint',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /checkpoint

Body parameter

{
  "checkpoint_type": "Stable-diffusion",
  "filenames": [
    {
      "filename": "v1-5-pruned-emaonly.safetensors",
      "parts_number": 5
    }
  ],
  "params": {
    "new_model_name": "test_api",
    "number": 1,
    "string": "abc"
  }
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "checkpoint": {
    "id": "id",
    "type": "Stable-diffusion",
    "s3_location": "s3://S3_Location",
    "status": "Initialed",
    "params": {}
  },
  "s3PresignUrl": [
    {
      "upload_id": "id,",
      "bucket": "bucket name,",
      "key": "key,"
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/checkpoint(put)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
"status": "Active",
"multi_parts_tags": {
  "v1-5-pruned-emaonly.safetensors.tar": [
    {
      "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
      "PartNumber": 1
    },
    {
      "ETag": "e4378bd84b0497559c55be8373cb79d0",
      "PartNumber": 2
    },
    {
      "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
      "PartNumber": 3
    },
    {
      "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
      "PartNumber": 4
    },
    {
      "ETag": "e613d37e5065b0cd63f1cad216423141",
      "PartNumber": 5
    }
  ]
}
}

r = requests.put('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/checkpoint', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
  "status": "Active",
  "multi_parts_tags": {
    "v1-5-pruned-emaonly.safetensors.tar": [
      {
        "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
        "PartNumber": 1
      },
      {
        "ETag": "e4378bd84b0497559c55be8373cb79d0",
        "PartNumber": 2
      },
      {
        "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
        "PartNumber": 3
      },
      {
        "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
        "PartNumber": 4
      },
      {
        "ETag": "e613d37e5065b0cd63f1cad216423141",
        "PartNumber": 5
      }
    ]
  }
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/checkpoint',
{
  method: 'PUT',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /checkpoint

Body parameter

{
  "checkpoint_id": "906a5a1f-6833-45aa-8a10-fb0e983e0eae",
  "status": "Active",
  "multi_parts_tags": {
    "v1-5-pruned-emaonly.safetensors.tar": [
      {
        "ETag": "cc95c41fa28463c8e9b88d67805f24e0",
        "PartNumber": 1
      },
      {
        "ETag": "e4378bd84b0497559c55be8373cb79d0",
        "PartNumber": 2
      },
      {
        "ETag": "815b68042f6ac5e60b9cff5c697ffea6",
        "PartNumber": 3
      },
      {
        "ETag": "2c6cfbd9bfbafd5664cdc8b3ba07df6d",
        "PartNumber": 4
      },
      {
        "ETag": "e613d37e5065b0cd63f1cad216423141",
        "PartNumber": 5
      }
    ]
  }
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "checkpoint": {
    "id": "id",
    "type": "Stable-diffusion",
    "s3_location": "s3://S3_Location",
    "status": "Active",
    "params": {}
  }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/checkpoints

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/checkpoints', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/checkpoints',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /checkpoints

Parameters

Name	In	Type	Required	Description
status	query	array[string]	false	none
types	query	array[string]	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "checkpoints": [
    {
      "id": "id",
      "s3Location": "s3://S3_Location",
      "type": "Stable-diffusion",
      "status": "Active",
      "name": [
        "object_1",
        "object_2"
      ],
      "created": 12341234
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/inference/v2/{id}/run(PUT)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

const inputBody = {
	"train_type": "Stable-diffusion",
	"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
	"params": {
		"training_params": {
			"data_tar_list": [
				"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
			],
			"class_data_tar_list": [
				""
			],
			"training_instance_type": "ml.g5.2xlarge"
		},
		"config_params": {
			"concepts_list": [{
				"class_data_dir": "",
				"class_guidance_scale": 7.5,
				"class_infer_steps": 40,
				"class_negative_prompt": "",
				"class_prompt": "",
				"class_token": "",
				"instance_prompt": "hanportraittest123",
				"num_class_images_per": 0,
				"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
				"instance_token": "",
				"is_valid": true,
				"n_save_sample": 1,
				"sample_seed": -1,
				"save_guidance_scale": 7.5,
				"save_infer_steps": 20,
				"save_sample_negative_prompt": "",
				"save_sample_prompt": "",
				"save_sample_template": ""
			}],
			"model_dir": "models/dreambooth/<your_model_name>",
			"model_name": "your_model_name",
			"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
			"num_train_epochs": 100,
			"use_lora": true,
			"revision": ""
		}
	}
}


r = requests.put('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/inference/v2/{id}/run', headers = headers, json = inputBody)

print(r.json())

Javascript example code：

const inputBody = '{
	"train_type": "Stable-diffusion",
	"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
	"params": {
		"training_params": {
			"data_tar_list": [
				"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
			],
			"class_data_tar_list": [
				""
			],
			"training_instance_type": "ml.g5.2xlarge"
		},
		"config_params": {
			"concepts_list": [{
				"class_data_dir": "",
				"class_guidance_scale": 7.5,
				"class_infer_steps": 40,
				"class_negative_prompt": "",
				"class_prompt": "",
				"class_token": "",
				"instance_prompt": "hanportraittest123",
				"num_class_images_per": 0,
				"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
				"instance_token": "",
				"is_valid": true,
				"n_save_sample": 1,
				"sample_seed": -1,
				"save_guidance_scale": 7.5,
				"save_infer_steps": 20,
				"save_sample_negative_prompt": "",
				"save_sample_prompt": "",
				"save_sample_template": ""
			}],
			"model_dir": "models/dreambooth/<your_model_name>",
			"model_name": "your_model_name",
			"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
			"num_train_epochs": 100,
			"use_lora": true,
			"revision": ""
		}
	}
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/inference/v2/{id}/run',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /inference/v2/{id}/run

Body parameter

{
	"train_type": "Stable-diffusion",
	"model_id": "7ec754d6-1f68-46ea-9cfe-efeeed0c986c",
	"params": {
		"training_params": {
			"data_tar_list": [
				"s3://<your_s3_bucket>/dataset/<your_dataset_name>"
			],
			"class_data_tar_list": [
				""
			],
			"training_instance_type": "ml.g5.2xlarge"
		},
		"config_params": {
			"concepts_list": [{
				"class_data_dir": "",
				"class_guidance_scale": 7.5,
				"class_infer_steps": 40,
				"class_negative_prompt": "",
				"class_prompt": "",
				"class_token": "",
				"instance_prompt": "hanportraittest123",
				"num_class_images_per": 0,
				"instance_data_dir": "s3://<your_s3_bucket>/dataset/<your_dataset_name>",
				"instance_token": "",
				"is_valid": true,
				"n_save_sample": 1,
				"sample_seed": -1,
				"save_guidance_scale": 7.5,
				"save_infer_steps": 20,
				"save_sample_negative_prompt": "",
				"save_sample_prompt": "",
				"save_sample_template": ""
			}],
			"model_dir": "models/dreambooth/<your_model_name>",
			"model_name": "your_model_name",
			"pretrained_model_name_or_path": "models/dreambooth/<your_model_name>/working",
			"num_train_epochs": 100,
			"use_lora": true,
			"revision": ""
		}
	}
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
	"statusCode": 200,
	"job": {
		"id": "81f00711-7cc3-4cac-90f3-13934f29524a",
		"status": "Initial",
		"trainType": "Stable-diffusion",
		"params": {
			"training_params": {
				...
			},
			"config_params": {
				...
			}
		},
		"input_location": "s3://<your_s3_bucket>/train/<your_model_name>/81f00711-7cc3-4cac-90f3-13934f29524a/input"
	},
	"s3PresignUrl": null
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/train(PUT)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
"status": "Training"
}

r = requests.put('https://{api_id}.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/train', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
  "status": "Training"
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/train',
{
  method: 'PUT',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /train

Body parameter

{
  "train_job_id": "b5183dd3-0279-46ff-b64e-6cd687c0fe71",
  "status": "Training"
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "job": {
    "id": "id",
    "status": "Training",
    "created": 12341234,
    "trainType": "Stable-diffusion",
    "params": {},
    "input_location": "s3://S3_Location"
  }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/trains(GET)

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/trains', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/trains',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /trains

Parameters

Name	In	Type	Required	Description
types	query	array[string]	false	none

Example responses

200 Response

{
  "statusCode": "200,",
  "trainJobs": [
    {
      "id": "id",
      "modelName": "model_name",
      "status": "Complete",
      "trainType": "Stable-diffusion",
      "created": 1234124,
      "sagemakerTrainName": "sagemaker_train_name"
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/dataset(POST)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

const inputBody = {
"dataset_name": "test_dataset",
"content": [
  {
    "filename": "/path/to/a/file.png",
    "name": "another_name",
    "type": "png"
  }
],
"params": {}
}

r = requests.post('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "dataset_name": "test_dataset",
  "content": [
    {
      "filename": "/path/to/a/file.png",
      "name": "another_name",
      "type": "png"
    }
  ],
  "params": {}
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/dataset',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /dataset

Body parameter

{
  "dataset_name": "test_dataset",
  "content": [
    {
      "filename": "/path/to/a/file.png",
      "name": "another_name",
      "type": "png"
    }
  ],
  "params": {}
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "datasetName": "dataset_name",
  "s3PresignUrl": [
    {
      "filename": "s3://S3_Location"
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/dataset(PUT)

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

inputBody = {
"dataset_name": "test_dataset",
"status": "Enabled"
}

r = requests.put('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "dataset_name": "test_dataset",
  "status": "Enabled"
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/dataset',
{
  method: 'PUT',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /dataset

Body parameter

{
  "dataset_name": "test_dataset",
  "status": "Enabled"
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "datasetName": "dataset_name",
  "status": "Enabled"
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/datasets(GET)

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/datasets', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/prod/datasets',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /prod/datasets

Parameters

Name	In	Type	Required	Description
dataset_status	query	array[string]	false	none

Example responses

200 Response

{
  "statusCode": 200,
  "datasets": [
    {
      "datasetName": "dataset_name",
      "s3": "s3://S3_Location",
      "status": "Enabled",
      "timestamp": 1234124
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/{dataset_name}/data

Code samples :

Python example code:

import requests
headers = {
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

r = requests.get('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/{dataset_name}/data', headers = headers)

print(r.json())

Javascript example code:


const headers = {
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/{dataset_name}/data',
{
  method: 'GET',

  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

GET /{dataset_name}/data

Parameters

Name	In	Type	Required	Description
dataset_name	path	string	true	none

Example responses

200 Response

{
  "statusCode": 200,
  "dataset_name": "dataset_name",
  "data": [
    {
      "key": "key",
      "name": "file name",
      "type": "image",
      "preview_url": "https://presigned_s3_url",
      "dataStatus": "Enabled"
    }
  ]
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Responses

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

Response Schema

/upload_checkpoint

Code samples :

Python example code:

import requests
headers = {
  'Content-Type': 'application/json',
  'Accept': 'application/json',
  'x-api-key': 'API_TOKEN_VALUE'
}

const inputBody = {
  "checkpointType":"Stable-diffusion",
  "modelUrl":["https://huggingface.co/xxx.safetensors","https://civitai.com/api/download/models/xxx"],
  "params":{"message":"description"}
}

r = requests.post('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/upload_checkpoint', headers = headers, json = inputBody)

print(r.json())

Javascript example code:

const inputBody = '{
  "checkpointType":"Stable-diffusion",
  "modelUrl":["https://huggingface.co/xxx/sd_xl_base_1.0.safetensors","https://civitai.com/api/download/models/xxx"],
  "params":{"message":"description"}
}';
const headers = {
  'Content-Type':'application/json',
  'Accept':'application/json',
  'x-api-key':'API_TOKEN_VALUE'
};

fetch('https://<Your API Gateway ID>.execute-api.<Your AWS Account Region>.amazonaws.com/{basePath}/dataset',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /upload_checkpoint

Body Parameters


{
  // checkpointType choices: "Stable-diffusion", "embeddings", "Lora", "hypernetworks", "ControlNet", "VAE"
  "checkpointType":"Stable-diffusion",
  "modelUrl":["https://huggingface.co/xxx.safetensors","https://civitai.com/api/download/models/xxx"],
  "params":{"message":"description"}
}

Parameters

Name	In	Type	Required	Description
body	body	object	false	none

Example responses

200 Response

{
    "statusCode": 200,
    "checkpoint": {
        "id": "07dbd061-1df8-463f-bc78-44a41956435c",
        "type": "Stable-diffusion",
        "s3_location": "s3://path",
        "status": "Active",
        "params": {
            "message": "description",
            "created": "2023-09-26 09:02:52.146566",
            "multipart_upload": {
                "bubble-gum-kaugummi-v20": null
            }
        }
    }
}

500 Response

{
  "statusCode": 500,
  "error": "error_message"
}

Response

Status	Meaning	Description	Schema
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

78 KiB Raw Blame History Unescape Escape

Stable Diffusion AWS extension API

Overview

User Scenarios

1. Deploy a new Endpoint

2. Upload a model

3. Train a model

4. Do Inference

/inference/test-connection

test middleware connection

Code samples :

Responses

/inference/v2

Code samples :

Parameters

Responses

/endpoints(POST)

Code samples :

Parameters

Responses

Response Schema

/endpoints(DELETE)

Code samples :

Parameters

Responses

Response Schema

/endpoints(GET)

Code samples :

Responses

Response Schema

/inference/list-inference-jobs

Code samples :

Responses

Response Schema

/inference/get-endpoint-deployment-job

Code samples :

Parameters

Responses

Response Schema

/inference/get-inference-job

Code samples :

Parameters

Responses

Response Schema

/inference/get-inference-job-image-output

Code samples :

Parameters

Responses

Response Schema

/inference/get-inference-job-param-output

Code samples :

Parameters

Responses

/inference/generate-s3-presigned-url-for-uploading

Code samples :

Parameters

Responses

/inference/get-texual-inversion-list

Code samples :

Responses

Response Schema

/inference/get-lora-list

Code samples :

Responses

Response Schema

/inference/get-hypernetwork-list

Code samples :

Responses

Response Schema

/inference/get-controlnet-model-list

Code samples :

Responses

Response Schema

/inference/run-model-merge

Code samples :

Responses

Response Schema

default

/model(POST)

Code samples :

78 KiB

Raw Blame History