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Stable Diffusion AWS extension API v2023-07-05

python
Python

javascript
Javascript

python

javascript

Stable Diffusion 亚马逊云科技插件 API

概述

本文档描述了Stable Diffusion 亚马逊云科技插件解决方案的所有 API。此解决方案包括两个部分，一部分是Stable Diffusion WebUI 扩展，它是基于 Gradio 的客户端，提供用户友好的界面，另一部分是称为中间件的资源部署在 AWS 云上，中间件提供了多个 API 接口，允许Stable Diffusion 亚马逊云科技插件客户端与 AWS 云上的服务进行交互，如 Sagemaker 和 S3，以执行模型更新/训练和推理操作。

为了支持不使用Stable Diffusion WebUI的用户，我们提供了本文档，列出了所有的 API 接口，以帮助用户了解如何调用 API 方法进行训练或推理。

在用户部署解决方案中间件 CloudFormation 后，用户可以在主堆栈的输出部分获取 API URL 和令牌。请参考此文档。

URL示例:

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

认证

API Key (api_key)
- API 调用时您需要在标头的x-api-key参数中配置此值。
Authorization
- 在 headers 中使用 Bearer Token

用户场景

1. 部署新的终端节点

调用 /endpoints 来创建一个新的 Sagemaker 终端节点，您需要指定两个用于创建的参数，一个是 instance_type，可选值为 "ml.g4dn.2xlarge"、"ml.g4dn.4xlarge"、"ml.g4dn.8xlarge"、"ml.g4dn.12xlarge"，另一个是 initial_instance_count，可选值为 1|2|3|4|5|6。

在调用 /endpoints 后，您需要调用 /endpoints 来列出所有终端节点的状态。通常需要超过10分钟时间才能使新的 Sagemaker 终端节点更改为 "InService" 状态。Sagemaker 终端节点只能在 "InService" 状态下用于推理。

如果终端节点处于失败状态，您可以调用 /inferences/{id} 并传递参数 jobID，响应将显示终端节点部署失败的原因，通常是由于 AWS 帐户配额限制引起的。

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

2. 上传模型

3. 训练模型

4. 进行推理

在 Sagemaker 终端节点处于 "InService" 状态之后，您可以调用 /inferences 来进行文本到图像或图像到图像的推理。您需要在请求的 POST 主体中的 "sagemaker_endpoint" 参数中指定终端节点的名称。其他所需参数位于 /inferences 中。

/inferences 将向客户端返回以下 JSON 结构：

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

然后，客户端可以调用 /inference/{id}，并将 inference_id 作为参数来查询推理作业的状态。

sequence diagram raw

title Do Inference

Client->Middleware:Call /inferences 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/{id} \n to query the inference job status Middleware->Client: return inference_id and the job status(inprocess | succeed | failure)

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

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

# API 列表

序号	HTTP 方法	API 名称	描述
1	GET	/ping	测试客户端是否可以连接到 API 并检查 API_TOKEN 是否正确
2	GET	/inferences	列出所有推理作业。
3	GET	/inferences/{id}	获取特定推理作业的详细信息。
23	PUT	/inferences/{id}/start	启动训练作业。
6	POST	/inferences	使用默认参数运行 SageMaker 推理
7	POST	/endpoints	部署 SageMaker 终端节点。
8	DELETE	/endpoints	删除 SageMaker 终端节点。
9	GET	/endpoints	列出所有终端节点部署作业。
12	GET	/inference/get-texual-inversion-list	获取文本反演列表。
13	GET	/inference/get-lora-list	获取 LoRa 列表。
14	GET	/inference/get-hypernetwork-list	获取超网络列表。
15	GET	/inference/get-controlnet-model-list	获取 ControlNet 模型列表。
16	POST	/inference/run-model-merge	运行模型合并。
17	POST	/models	创建新模型。
18	PUT	/models/{id}	上传模型文件。
19	GET	/models	列出所有模型。
20	GET	/checkpoints	获取检查点。
21	PUT	/checkpoints/{id}	更新检查点。
22	GET	/checkpoints	列出所有检查点。
24	PUT	/trainings	更新训练作业。
25	GET	/trainings	列出所有训练作业。
26	POST	/datasets	创建新数据集。
27	PUT	/datasets/{name}	更新数据集。
28	GET	/datasets	列出所有数据集。
29	GET	/datasets/{name}	获取特定数据集的数据。

/ping

测试中间件连接状态

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /ping

响应示例

200 响应

{
    "message": "Success"
}

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/inferences

文生图，通过文字生成图片

示例：

Python示例代码：

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}/inferences", headers = headers, json = body)

print(r.json())

Javascript示例代码：

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}/inferences",
{
  method: "POST",
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /inferences

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
}

参数

Parameter example

Below JSON shows the parameters with default value. sagemaker_endpoint_name, task_type, prompt and Stable-Diffusion is 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": []
    }
  }
}

响应示例

200 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/endpoints(POST)

创建 SageMaker 节点

示例：

Python示例代码：

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示例代码：

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参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

null

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/endpoints(DELETE)

删除 Endpoints

示例：

Python示例代码：

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示例代码：

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 参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

Endpoint deleted

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/endpoints(GET)

列出所有终端节点部署作业

示例：

Python示例代码：

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示例代码：


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

响应示例

200 响应

[
  {
    "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"
  }
]

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/inferences

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /inferences

响应示例

200 响应

[
  {
    "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"
    ]
  }
]

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

/inferences/{id}

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /inferences/{id}

参数

名称	位置	数据类型	是否需要填写	描述
jobID	query	string	false	none

响应示例

200 响应

{
  "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"
  ]
}

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline
422	Unprocessable Entity	Validation Error	HTTPValidationError

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

示例：

Python示例代码：

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示例代码：


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

参数

名称	位置	数据类型	是否需要填写	描述
s3_bucket_name	query	string	false	none
key	query	string	false	none

响应示例

200 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful Response	string
422	Unprocessable Entity	Validation Error	HTTPValidationError

/inference/run-model-merge

示例：

Python示例代码：

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示例代码：


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

响应示例

200 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful Response	Inline

default

/model(POST)

示例：

Python示例代码：

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示例代码：

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 /models

Body 参数

{
  "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
    }
  }
}

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

{
  "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 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/models/{id} (PUT)

示例：

Python示例代码：

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示例代码：

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 /models/{id}

Body 参数

{
  "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
      }
    ]
  }
}

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/models (GET)

示例：

Python示例代码：

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示例代码：


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

参数

名称	位置	数据类型	是否需要填写	描述
types	query	array[string]	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/checkpoints

示例：

Python示例代码：

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示例代码：

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.{region}.amazonaws.com/{basePath}/checkpoint',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

POST /checkpoints

Body 参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

{
  "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 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/checkpoints/{id} (put)

示例：

Python示例代码：

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.{region}.amazonaws.com/{basePath}/checkpoint', headers = headers, json = inputBody)

print(r.json())

Javascript示例代码：

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.{region}.amazonaws.com/{basePath}/checkpoint',
{
  method: 'PUT',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /checkpoints/{id}

Body 参数

{
  "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
      }
    ]
  }
}

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/checkpoints

列出所有检查点

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /checkpoints

参数

名称	位置	数据类型	是否需要填写	描述
status	query	array[string]	false	none
types	query	array[string]	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/inferences/{id}/start

示例：

Python示例代码：

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.{region}.amazonaws.com/{basePath}/inferences/{id}/start', headers = headers, json = inputBody)

print(r.json())

Javascript示例代码：

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.{region}.amazonaws.com/{basePath}/inferences/{id}/start',
{
  method: 'POST',
  body: inputBody,
  headers: headers
})
.then(function(res) {
    return res.json();
}).then(function(body) {
    console.log(body);
});

PUT /inferences/{id}/start

Body 参数

{
	"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": ""
		}
	}
}

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

{
	"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 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/trainings/{id} (PUT)

示例：

Python示例代码：

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.{region}.amazonaws.com/{basePath}/train', headers = headers, json = inputBody)

print(r.json())

Javascript示例代码：

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

PUT /trainings/{id}

Body 参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/trainings (GET)

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /trainings

参数

名称	位置	数据类型	是否需要填写	描述
types	query	array[string]	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/datasets (POST)

创建新数据集

示例：

Python示例代码：

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://{api_id}.execute-api.{region}.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)

print(r.json())

Javascript示例代码：

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://{api_id}.execute-api.{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 参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/datasets/{name} (PUT)

示例：

Python示例代码：

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.{region}.amazonaws.com/{basePath}/dataset', headers = headers, json = inputBody)

print(r.json())

Javascript示例代码：

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.{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 参数

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

参数

名称	位置	数据类型	是否需要填写	描述
body	body	object	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/datasets (GET)

列出所有数据集

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

fetch('https://<Your API Gateway ID>.execute-api.{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

参数

名称	位置	数据类型	是否需要填写	描述
dataset_status	query	array[string]	false	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

响应结构

/datasets/{name}

示例：

Python示例代码：

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

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

print(r.json())

Javascript示例代码：


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

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

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

GET /datasets/{name}

参数

名称	位置	数据类型	是否需要填写	描述
dataset_name	path	string	true	none

响应示例

200 响应

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

500 响应

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

响应

状态码	含义	描述	结构
200	OK	Successful response	Inline
500	Internal Server Error	Error response	Inline

59 KiB Raw Blame History Unescape Escape

Stable Diffusion 亚马逊云科技插件 API

概述

用户场景

1. 部署新的终端节点

2. 上传模型

3. 训练模型

4. 进行推理

/ping

测试中间件连接状态

示例：

响应

/inferences

示例：

参数

响应

/endpoints(POST)

示例：

参数

响应

/endpoints(DELETE)

示例：

参数

响应

/endpoints(GET)

示例：

响应

/inferences

示例：

响应

/inferences/{id}

示例：

参数

响应

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

示例：

参数

响应

/inference/run-model-merge

示例：

响应

default

/model(POST)

示例：

参数

响应

响应结构

/models/{id} (PUT)

示例：

参数

响应

响应结构

/models (GET)

示例：

参数

响应

响应结构

/checkpoints

示例：

参数

响应

响应结构

/checkpoints/{id} (put)

示例：

参数

响应

响应结构

/checkpoints

示例：

参数

响应

响应结构

/inferences/{id}/start

示例：

参数

响应

响应结构

/trainings/{id} (PUT)

示例：

参数

59 KiB

Raw Blame History