Midjourney Imagine API 申请及利用

Midjourney Imagine API 申请及利用

Midjourney 是一款非常强大的 AI 绘图工具，只要输入关键字，就能在短短一两分钟天生十分风雅的图像。Midjourney 以其出色的绘图本领在业界独树一帜，现在，Midjourney 早已在各个行业和领域广泛应用，其影响力愈发显著。
本文档重要介绍 Midjourney API 中 Imagine 操作的利用流程，利用它我们可以轻松通过文本天生所必要的图像。
申请流程

要利用 Midjourney Imagine API，起首可以到 Midjourney Imagine API 页面点击「Acquire」按钮，获取请求所必要的凭证：

如果你尚未登录或注册，会主动跳转到登录页面邀请您来注册和登录，登录注册之后会主动返回当前页面。
在首次申请时会有免费额度赠送，可以免费利用该 API。
根本利用

接下来就可以在界面上填写对应的内容，如图所示：

在第一次利用该接口时，我们至少必要填写两个内容，一个是 authorization，直接在下拉列表里面选择即可。另一个参数是 prompt， prompt 就是我们想天生的图片描述内容，发起用英文描述，画的图会更精确效果更好，这里我们用了示例内容 Lamborghini speeds inside a volcano，代表要画一个兰博基尼在火山飞驰。
同时您可以注意到右侧有对应的调用代码天生，您可以复制代码直接运行，也可以直接点击「Try」按钮进行测试。

调用之后，我们发现返回效果如下：
json { "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234197197067915365/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_f47263b6-ff92-44a3-88ee-51cf0e706aae.png?ex=662fdb36&is=662e89b6&hm=ca9be54907726937ed02517a13466bef2afb2825b7cda4b313de56a3c3310d0d&width=1024&height=1024", "image_width": 1024, "image_height": 1024, "image_id": "1234197197067915365", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234197197067915365/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_f47263b6-ff92-44a3-88ee-51cf0e706aae.png?ex=662fdb36&is=662e89b6&hm=ca9be54907726937ed02517a13466bef2afb2825b7cda4b313de56a3c3310d0d&", "raw_image_width": 2048, "raw_image_height": 2048, "progress": 100, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ], "task_id": "1bae3bec-3ac4-4180-a148-74ee6cb68b98", "success": true }
返回效果一共有多个字段，介绍如下：

• task_id，天生此图像使命的 ID，用于唯一标识此次图像天生使命。
  
• image_id，图片的唯一标识，在下次必要对图片进行变换操作时必要传此参数。
  
• image_url，缩略图的 URL，直接打开即可查看天生的效果。
  
• image_width：缩略图的像素宽度。
  
• image_height：缩略图的像素高度。
  
• raw_image_url：原图的 URL，和缩略图内容一样，但相比缩略图更加高清，加载速度会更慢一些。
  
• raw_image_width：原图的像素宽度。
  
• raw_image_height：原图的像素高度。
  
• actions，可以对天生的图片进行的进一步操作列表。这里一共列了 8 个，其中 upscale 代表放大，variation 代表变换。以是 upscale1 代表的就是对左上角第一张图片进行放大操作，variation3 就是代表根据左下角第三张图片进行变换操作。
  

打开 image_url 或者 raw_image_url 所对应的链接，可以发现如图所示。

可以看到，这里天生了一张 2x2 的预览图。到现在为止，第一次 API 调用就完成了。
图像放大与变换

下面我们实行针对当前天生的照片进行进一步的操作，好比我们以为右上角第二张的图片还不错，但我们想进行一些变换微调，那么就可以进一步将 action 填写为 variation2，同时将 image_id 通报即可：

这时候得到的效果如下：
json { "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234201336543969401/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_10dc56a7-ec16-4bac-878e-2338f2ae5f5d.png?ex=662fdf10&is=662e8d90&hm=9aec96bca35ae20b6f9ab536101b9c4ea255eb6216cbf7000ac554937da071f3&width=1024&height=1024", "image_width": 1024, "image_height": 1024, "image_id": "1234201336543969401", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234201336543969401/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_10dc56a7-ec16-4bac-878e-2338f2ae5f5d.png?ex=662fdf10&is=662e8d90&hm=9aec96bca35ae20b6f9ab536101b9c4ea255eb6216cbf7000ac554937da071f3&", "raw_image_width": 2048, "raw_image_height": 2048, "progress": 100, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ], "task_id": "f4961620-1104-409f-9dc1-ba3ed15c2f4d", "success": true }
打开 image_url，新天生的图片如下所示：

可以看到，针对上一张右上角的图片，我们再次得到了四张类似的照片。
这时候我们可以挑选其中一张进行精细化地放大操作，好比选第四张，那就可以 action 传入 upscale4，通过 image_id 再次传入当前图像的 ID 即可。

   注意： upscale 操作相比 variation 来说，Midjourney 的耗时会更短一些。
  返回效果如下：
json { "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234202545208033400/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_34edc3f5-2bd0-4f5b-a372-03270b02289b.png?ex=662fe031&is=662e8eb1&hm=f8006c4d33a03dfd027dffe4eb46ab0d113a4910aef07497f0b335c8998b7858&width=512&height=512", "image_width": 512, "image_height": 512, "image_id": "1234202545208033400", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1233387694839697411/1234202545208033400/36rgqit64j90qptsxnyq_Lamborghini_speeds_inside_a_volcano_id0494_34edc3f5-2bd0-4f5b-a372-03270b02289b.png?ex=662fe031&is=662e8eb1&hm=f8006c4d33a03dfd027dffe4eb46ab0d113a4910aef07497f0b335c8998b7858&", "raw_image_width": 1024, "raw_image_height": 1024, "progress": 100, "actions": [ "upscale_2x", "upscale_4x", "variation_subtle", "variation_strong", "zoom_out_2x", "zoom_out_1_5x", "pan_left", "pan_right", "pan_up", "pan_down" ], "task_id": "03f62b17-a6f1-4c8e-9b4d-1fc7bd5b1180", "success": true }
其中 image_url 如图所示：

这样我们就乐成得到了一张兰博基尼的照片。
同时注意到 actions 里面又包含了几个可进行的操作，介绍如下：

• upscale_2x：对画面放大 2 倍，得到 2 倍高清图。
  
• upscale_4x：对画面放大 4 倍，得到 4 倍高清图。
  
• zoom_out_2x：对画面进行缩小两倍操作（四周地域添补）。
  
• zoom_out_1_5x：对画面进行缩小 1.5 倍操作（四周地域添补）。
  
• pan_left：对画面进行左偏移操作。
  
• pan_right：对画面进行右便宜操作。
  
• pan_up：对画面进行上偏移操作。
  
• pan_down：对画面进行下偏移操作。
  

可以继续按照上述流程传入对应的变换指令进行连续生图操作。
图像改写（垫图）

该 API 也支持图像改写，俗称垫图，我们可以输入一张图片 URL 以及必要改写的描述文字，该 API 就可以返回改写后的图片。
   注意：输入的图片 URL 必要是一张纯图片，不能是一个网页里面展示一张图片，否则无法进行图像改写。发起利用图床来上传获取图片的 URL。
  例如，我们这里有一张公路夕阳的图片，公路旁边有一些树木和楼房，如图所示：

现在我们想在它的基础上改写成海滩旁边，同时放一辆汽车停在路边。我们就可以构造如下的 prompt：
bash https://cdn.zhishuyun.com/eced6c2f-5be9-4e16-ad6c-c8ab4773cce1.png an illustration of a car parked on the beach --iw 2
可以看到，我们的 prompt 的最开头是一个 HTTPS 开头的图片链接，然后接着加一个空格，背面跟上 prompt 文字的内容。这里我们还用了额外的一些高级参数，如 —iw 2 来调整图片的权重。
我们可以将如上内容作为一个整体，通报给 prompt 字段，如图所示：

输出效果如下：
```bash { "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234427310434947145/1234539663515975690/atmateosa5693_An_illustration_of_a_car_parked_on_the_beach_id26_cc8650ec-7e4b-4685-8911-78172430d8a7.png?ex=66311a28&is=662fc8a8&hm=c39707a1f22bc7f12874060ea6ed58ba37c188139ccc9a13c61ed9f37e66ea74&width=1456&height=816", "image_width": 1456, "image_height": 816, "image_id": "1234539663515975690", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234427310434947145/1234539663515975690/atmateosa5693_An_illustration_of_a_car_parked_on_the_beach_id26_cc8650ec-7e4b-4685-8911-78172430d8a7.png?ex=66311a28&is=662fc8a8&hm=c39707a1f22bc7f12874060ea6ed58ba37c188139ccc9a13c61ed9f37e66ea74&", "raw_image_width": 2912, "raw_image_height": 1632, "progress": 100, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ], "task_id": "24a79e8b-a79d-471a-aef7-089dc0627ee8", "success": true }
```
这时候我们就得到了如下天生的图片：

可以看到，在原来的图片整体风格和构图不变的前提下，整个场景变成了海滩旁边，同时公路上还出现了汽车，这就是 Prompt with Image。
图像融合

该 API 也支持图像融合，我们可以传入多张图片，以实现差别的图片融合效果。
好比说这里我们一共有两张图片，一张是一只玩具熊，另一张是一个电锯，分别如图所示：

现在我们想把二者融合起来，让这只熊拿着这个电锯，怎么做呢？
我们可以构造如下的 prompt：
bash https://cdn.zhishuyun.com/ea837b35-6d3d-437a-b113-2e96e8a0fac2.png https://cdn.zhishuyun.com/8a700c6a-4eb6-4f41-af42-948c64571ac9.png The bear is holding the chainsaw --iw 2
可以发现，和 Image with Prompt 类似，我们这里将多张图片 URL 放在了 prompt 开头，并以空格分隔，最后再加上文字 prompt，将如上内容作为一个整体通报给 prompt 参数，运行效果如下：
```bash { "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234547236830973972/kcisok_The_bear_is_holding_the_chainsaw_id8873344_ad605bc4-ba19-4807-b94f-367dab672f7a.png?ex=66312136&is=662fcfb6&hm=0fb1e2261c9a30b04de9da9b23b7562eb73677f1bbda1fae52c7243b12d25aac&width=1024&height=1024", "image_width": 1024, "image_height": 1024, "image_id": "1234547236830973972", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234547236830973972/kcisok_The_bear_is_holding_the_chainsaw_id8873344_ad605bc4-ba19-4807-b94f-367dab672f7a.png?ex=66312136&is=662fcfb6&hm=0fb1e2261c9a30b04de9da9b23b7562eb73677f1bbda1fae52c7243b12d25aac&", "raw_image_width": 2048, "raw_image_height": 2048, "progress": 100, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ], "task_id": "891f2645-ee15-4c7b-ac24-d98163c8e57e", "success": true }
```
我们就得到了如下效果：

可以看到，我们就乐成实现了图片融合。
   注意：图片融合最多可以支持 5 个图片 URL 作为输入，也就是最多支持 5 张图片融合，输入格式同上。
  局部变换

该 API 也支持图像的局部绘图功能，但只支持在上文内容下天生的图片，我们可以传入一张天生图片的的唯一ID，局部重绘的行为参数 action 以及必要重绘地域的掩码mask，以实现在改掩码地域进行重绘。
好比说这里我们有一张天生关于猫的图片：

现在我们想对这个猫脸进行重绘，怎么做呢？
起首我们必要获取该地域的掩码mask，此mask是通过灰度图片进行Base64编码得来的，下面提供一些获取掩码的工具代码：
Python获取掩码示例代码：
```python import sys import os from PySide6.QtWidgets import * from PySide6.QtGui import QPainter, QMouseEvent, QPen, QColor, QImage from PySide6.QtCore import Qt, QPoint
class DrawingWidget(QWidget): def init(self, imagePath): super().init() self.setAttribute(Qt.WA_StaticContents) self.background_image = QImage(imagePath) imageSize = self.background_image.size()*0.8 self.setFixedSize(imageSize) self.foreground_image = QImage(self.size(), QImage.Format_ARGB32) self.foreground_image.fill(Qt.transparent) self.drawing = False self.lastPoint = QPoint() self.pen_color = QColor(255, 255, 255, 255) self.pen_size = 50

1. def set_pen_size(self, size):
2.     self.pen_size = size
4. def mousePressEvent(self, event: QMouseEvent):
5.     if event.button() == Qt.LeftButton:
6.         self.drawing = True
7.         self.lastPoint = event.pos()
9. def mouseMoveEvent(self, event: QMouseEvent):
10.     if event.buttons() & Qt.LeftButton and self.drawing:
11.         painter = QPainter(self.foreground_image)
12.         painter.setRenderHint(QPainter.Antialiasing, True)
13.         pen = QPen(self.pen_color, self.pen_size,
14.                    Qt.SolidLine, Qt.RoundCap, Qt.RoundJoin)
15.         painter.setPen(pen)
16.         painter.drawLine(self.lastPoint, event.pos())
17.         self.lastPoint = event.pos()
18.         self.update()
20. def mouseReleaseEvent(self, event: QMouseEvent):
21.     if event.button() == Qt.LeftButton and self.drawing:
22.         self.drawing = False
24. def paintEvent(self, event):
25.     canvasPainter = QPainter(self)
26.     canvasPainter.drawImage(
27.         self.rect(), self.background_image, self.background_image.rect())
28.     canvasPainter.drawImage(
29.         self.rect(), self.foreground_image, self.foreground_image.rect())
31. def save_image(self, path):
32.     self.foreground_image.save(path)

复制代码

class MainWindow(QDialog): def init(self, imagePath): super().init() self.setWindowTitle("mask tool") self.drawing_widget = DrawingWidget(imagePath) self.currentPath = os.getcwd().replace("\", "/") self.tempPath = self.currentPath + "/temp.jpg" self.projectPath = "" self.setDone = False

1. def init_ui(self):
2.     layout = QVBoxLayout()
3.     layout.addWidget(self.drawing_widget)
4.     controls_layout = QHBoxLayout()
5.     size_label = QLabel("pen size:")
6.     controls_layout.addWidget(size_label)
7.     self.size_slider = QSlider(Qt.Horizontal)
8.     self.size_slider.setMinimum(100)
9.     self.size_slider.setMaximum(400)
10.     self.size_slider.setValue(400)
11.     self.size_slider.valueChanged.connect(self.update_pen_size)
12.     controls_layout.addWidget(self.size_slider)
13.     self.lineEdit_addPromp = QLineEdit()
14.     layout.addWidget(self.lineEdit_addPromp)
15.     save_button = QPushButton("   Start partial redrawing   ")
16.     save_button.clicked.connect(self.save_image)
17.     controls_layout.addWidget(save_button)
18.     dont_button = QPushButton("   Cancel partial redraw   ")
19.     dont_button.clicked.connect(self.dont_image)
20.     controls_layout.addWidget(dont_button)
21.     layout.addLayout(controls_layout)
22.     self.setLayout(layout)
24. def update_pen_size(self):
25.     size = self.size_slider.value()
26.     self.drawing_widget.set_pen_size(size)
28. def save_image(self):
29.     tempImage = self.currentPath + "/temp.jpg"
30.     self.drawing_widget.save_image(self.tempPath)
31.     self.prompt = self.lineEdit_addPromp.text()
32.     self.setDone = True
33.     self.close()
35. def dont_image(self):
36.     self.setDone = False
37.     self.close()
39. def closeEvent(self, event):
40.     if self.setDone:
41.         pass
42.     else:
43.         self.setDone = False
44.     event.accept()

复制代码

if name == 'main': imagePath = "test.png" app = QApplication(sys.argv) mainWindow = MainWindow(imagePath) mainWindow.init_ui() mainWindow.exec() ```
通过以上代码可以获得掩码图片，在这个过程中我们必要注意掩码图片必须与原图片一样的尺寸，然后掩码图片中白色的地域就是必要重绘的地域，下面展示猫图片必要重绘的掩码图片与原图的对比：
原图片：

掩码图片：

最后我们还必要将掩码图片转换为基于Base64的编码，接下来提供转换Base64编码的代码：
Python转换Base64示例代码：
```python import cv2 import base64
image_path = 'temp.jpg' gray_image = cv2.imread(image_path) _, buffer = cv2.imencode('.jpg', gray_image) base64_encoded = base64.b64encode(buffer).decode('utf-8') with open('grayscale_image_base64.txt', 'w') as f: f.write(base64_encoded)
print("success!") ```
   注意：上述 Python 代码描述了 mask 的天生过程，如果您要将其对接到您的产品中，请根据其原理编写对应语言的代码。
  通过以上代码，我们获得了必要重绘的掩码 mask ，下面我们还必要必要将参数 action 设置为 variation_region ，天生图片的ID image_id （获取该参数参考上文的内容），以及传入对应的掩码 mask ，别的参数信息如下：

• action：对图片进行操作的行为，此处为 variation_region ，表示对图片进行局部重绘。
  
• prompt：对图像进行局部重绘的描述词（可选参数）。
  
• image_id：图片的唯一标识，方便对图像进行局部重绘。
  
• mask：图片对应的掩码地域的base64编码（图片是上面image_id指定的）。
  

因此根据以上规则我们必要设置精确的的参数，参数 prompt 是一个非必填参数，这里为了方便对比将掩码地域的 prompt 设置为 A cute cat ，具体的参数设置如下图所示：

代码示例

可以发现，在页面右侧已经主动天生了各种语言的代码，如图所示：

部分代码示例如下：
CURL

bash curl -X POST 'https://api.acedata.cloud/midjourney/imagine' \ -H 'accept: application/json' \ -H 'authorization: Bearer {token}' \ -H 'content-type: application/json' \ -d '{ "prompt": "A cute cat ", "action": "variation_region", "image_id": "1265875488702726144", "mask": "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" }'
Python

```python import requests
url = "https://api.acedata.cloud/midjourney/imagine"
headers = { "accept": "application/json", "authorization": "Bearer {token}", "content-type": "application/json" }
payload = { "prompt": "A cute cat ", "action": "variation_region", "image_id": "1265875488702726144", "mask": "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AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/Z" }
response = requests.post(url, json=payload, headers=headers) print(response.text) ```
响应示例

请求乐成后，API 将返回换脸后端图片效果信息。例如：
json { "image_url": "https://platform.cdn.acedata.cloud/midjourney/6c9450d8-1c22-4f85-a527-e7a7bfb4a61b.png?imageMogr2/thumbnail/!50p", "image_width": 1024, "image_height": 1024, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ], "raw_image_url": "https://platform.cdn.acedata.cloud/midjourney/6c9450d8-1c22-4f85-a527-e7a7bfb4a61b.png", "raw_image_width": 2048, "raw_image_height": 2048, "progress": 100, "image_id": "1265876571323891712", "task_id": "6c9450d8-1c22-4f85-a527-e7a7bfb4a61b", "success": true }
可以发现这是对掩码地域的图像进行了重绘操作，返回的效果与上文的是同等的，效果如下图所示：

可以看到，我们就乐成实现了对天生的图片的自界说地域进行局部重绘。
异步回调

由于 Midjourney 天生图片必要等候一段时间，以是本 API 也默认计划为了长等候模式。但在部分场景下，长等候可能会带来一些额外的资源开销，因此本 API 也提供了异步 Webhook 回调的方式，当图片天生乐成或失败时，其效果都会通过 HTTP 请求的方式发送到指定的 Webhook 回调 URL。回调 URL 接收到效果之后可以进行进一步的处置惩罚。
下面演示具体的调用流程。
起首，Webhook 回调是一个可以接收 HTTP 请求的服务，开发者应该更换为本身搭建的 HTTP 服务器的 URL。此处为了方便演示，利用一个公开的 Webhook 样例网站 Webhook.site - Test, transform and automate Web requests and emails，打开该网站即可得到一个 Webhook URL，如图所示：

将此 URL 复制下来，就可以作为 Webhook 来利用，此处的样例为 https://webhook.site/995d0a91-d737-40a7-a3b9-5baf68ed924c。
接下来，我们可以设置字段 callback_url 为上述 Webhook URL，同时填入 prompt，如图所示：

点击测试之后会立刻得到一个 task_id 的响应，用于标识当前天生使命的 ID，如图所示：

稍等片刻，等图片天生结束，可以发发现 Webhook URL 收到了一个 HTTP 请求，如图所示：

其效果就是当前使命的效果，内容如下：
json { "success": true, "task_id": "f6e39eaf-652a-4bf5-a15c-79d8b143b80a", "image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234551030549839932/kcisok_A_cat_sitting_on_a_table_id2724480_591c5c85-ec80-42ab-9fe5-9adfbed192e4.png?ex=663124be&is=662fd33e&hm=da725eb74aae375d60beec38b4cd26c5a7b373b1662f222ff838a8ea6fd5e798&width=1024&height=1024", "image_width": 1024, "image_height": 1024, "image_id": "1234551030549839932", "raw_image_url": "https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234551030549839932/kcisok_A_cat_sitting_on_a_table_id2724480_591c5c85-ec80-42ab-9fe5-9adfbed192e4.png?ex=663124be&is=662fd33e&hm=da725eb74aae375d60beec38b4cd26c5a7b373b1662f222ff838a8ea6fd5e798&", "raw_image_width": 2048, "raw_image_height": 2048, "progress": 100, "actions": [ "upscale1", "upscale2", "upscale3", "upscale4", "reroll", "variation1", "variation2", "variation3", "variation4" ] }
其中 success 字段标识了该使命是否实行乐成，如果实行乐成，还会有同样的 actions, image_id, image_url 字段，和上文介绍的返回效果是一样的，另外还有 task_id 用于标识使命，以实现 Webhook 效果和最初 API 请求的关联。
如果图片天生失败，Webhook URL 则会收到类似如下内容：
json { "success": false, "task_id": "7ba0feaf-d20b-4c22-a35a-31ec30fc7715", "error": { "code": "bad_request", "message": "Unrecognized argument(s): `-c`, `x`" } }
这里的 success 字段会是 false，同时还会有 error.code 和 error.message 字段描述了使命错误的详情信息，Webhook 服务器根据对应的效果进行处置惩罚即可。
流式输出

Midjourney 官方在天生图片的时候是有进度的，在最开始是一张模糊的照片，然后经过频频迭代之后，图片徐徐变得清楚，最后得到完备的图片。
以是，一张图片的天生过程约莫可以分为「发送下令」->「开始生图（多次迭代徐徐清楚）」->「生图完毕」的阶段。
在没开启流式输出的情况下，本 API 从发起请求到返回效果，实际上是从上述「发送下令」->「生图完毕」的全过程，中心生图的过程也全被包含在里面，由于 Midjourney 本身天生图片速度较慢，整个过程约莫必要等候一分钟或更久。
以是为了更好的用户体验，本 API 支持流式输出，即当「开始生图」的时候就开始返回效果，每当绘制进度有变革，就会流式将效果输出，直至生图结束。
如果想流式返反响应，可以更改请求头里面的 accept 参数，修改为 application/x-ndjson，不过调用代码必要有对应的更改才气支持流式响应。
Python 样例代码：
```python import requests
url = 'https://api.acedata.cloud/midjourney/imagine' headers = { 'content-type': 'application/json', 'accept': 'application/x-ndjson', 'authorization': 'Bearer {token}' } body = { "prompt": "a beautiful cat --v 6" } r = requests.post(url, headers=headers, json=body, stream=True) for line in r.iter_lines(): print(line.decode()) ```
运行效果：
json {"image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558451443699803/eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc_grid_0.webp?ex=66312ba7&is=662fda27&hm=4625d5f12158bffc07c4faaf6ce75af6f1396122f148b33b91f3e20b48fecc8b&width=256&height=256","image_width":256,"image_height":256,"image_id":"1234558451443699803","raw_image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558451443699803/eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc_grid_0.webp?ex=66312ba7&is=662fda27&hm=4625d5f12158bffc07c4faaf6ce75af6f1396122f148b33b91f3e20b48fecc8b&","raw_image_width":512,"raw_image_height":512,"progress":35,"actions":[],"task_id":"49589d2c-b6b3-4fbf-9f82-93068509c76f","success":true} {"image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558458595115149/eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc_grid_0.webp?ex=66312ba9&is=662fda29&hm=9af53fa645127131a88dfbb3930add7abda710c12a3d6c30c457d6a067b36bab&width=256&height=256","image_width":256,"image_height":256,"image_id":"1234558458595115149","raw_image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558458595115149/eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc_grid_0.webp?ex=66312ba9&is=662fda29&hm=9af53fa645127131a88dfbb3930add7abda710c12a3d6c30c457d6a067b36bab&","raw_image_width":512,"raw_image_height":512,"progress":75,"actions":[],"task_id":"49589d2c-b6b3-4fbf-9f82-93068509c76f","success":true} {"image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558483408490566/kcisok_A_landscape_painting_of_a_beautiful_sunset_id5963392_eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc.png?ex=66312baf&is=662fda2f&hm=185ea8f130806bf8bd96911bd251808455fd65596edcdb459f9b3cfd7860387c&width=1024&height=1024","image_width":1024,"image_height":1024,"image_id":"1234558483408490566","raw_image_url":"https://midjourney.cdn.acedata.cloud/attachments/1234291876639674388/1234558483408490566/kcisok_A_landscape_painting_of_a_beautiful_sunset_id5963392_eae94f0f-0ba5-4b3c-9bad-59fb33ac2cbc.png?ex=66312baf&is=662fda2f&hm=185ea8f130806bf8bd96911bd251808455fd65596edcdb459f9b3cfd7860387c&","raw_image_width":2048,"raw_image_height":2048,"progress":100,"actions":["upscale1","upscale2","upscale3","upscale4","reroll","variation1","variation2","variation3","variation4"],"task_id":"49589d2c-b6b3-4fbf-9f82-93068509c76f","success":true}
可以看到，启用流式输出之后，返回效果就是逐行的 JSON 了。
在 Node.js 环境中，实今世码可写为如下：
```javascript const axios = require("axios");
const url = "https://api.acedata.cloud/midjourney/imagine"; const headers = { "content-type": "application/json", accept: "application/x-ndjson", authorization: "Bearer {token}", }; const body = { prompt: "a beautiful cat --v 6", action: "generate", };
axios .post(url, body, { headers: headers, responseType: "stream" }) .then((response) => { console.log(response.status); response.data.on("data", (chunk) => { console.log(chunk.toString()); }); }) .catch((error) => { console.error(error); }); ```
这些示例运行的效果都是相似的。
请注意，流式输出效果中有一个称为 progress 的字段，表示天生进度，范围从 0 到 100。如果必要，您可以在页面上显示这些信息。
   注意：当天生未完全完成时，actions 字段为空，表示无法对中心图像实行进一步处置惩罚操作。天生完成后，在天生过程中天生的 image_url 将被烧毁。
  别的，您可以通过指定 accept=application/x-ndjson 的请求头和 callback_url 的请求体，将流式效果与异步回调联合起来，然后 callback_url 可以接收到多个流式效果的 POST 请求。

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