> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anyfast.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini 3 Pro Image (Stream)

> 通过 Gemini API 调用 Google Gemini 3 Pro Image 进行流式图片生成。SSE 实时推送思考 chunk 与图片 chunk。

Gemini 3 Pro Image (Stream) 通过 Anyfast 以原生 Gemini API 提供服务，支持实时 SSE 流式返回图片生成结果。思考 chunk 先行推送，最终图片 chunk 紧随其后。

<Warning>
  流式输出需要 **Direct** 分组令牌，需在控制台选择 **Direct** 分组。
</Warning>

## 核心能力

* **SSE 流式输出** — 实时推送思考 chunk 与图片 chunk
* **思考模式** — 内部推理 chunk（`thought: true`）在图片之前流式输出
* **文生图** — 根据文本描述生成图片
* **图片编辑** — 在 `inline_data` 中传入参考图，配合文字指令进行编辑
* **宽高比控制** — `1:1`、`4:3`、`3:4`、`16:9`、`9:16`
* **分辨率控制** — `1K`（\~1024px）、`2K`（\~2048px）、`4K`（\~4096px，按长边）

## SSE 响应格式

流式端点返回换行分隔的 SSE 数据行，每行以 `data:` 开头，后跟 JSON 对象。共有三种 chunk 类型：

1. **思考 chunk** — 最先到达；`parts[0].thought` 为 `true`
2. **图片 chunk** — 包含 `parts[0].inlineData`，含 `mimeType` 和 base64 `data`（注意：流式响应中为驼峰命名）
3. **最终用量 chunk** — 包含顶层 `usageMetadata`，含 `thoughtsTokenCount` 及各模态 token 详情

```
data: {"candidates":[{"content":{"role":"model","parts":[{"text":"...","thought":true}]}}],"usageMetadata":{"trafficType":"ON_DEMAND"},"modelVersion":"gemini-3-pro-image","createTime":"...","responseId":"..."}

data: {"candidates":[{"content":{"role":"model","parts":[{"inlineData":{"mimeType":"image/png","data":"<base64>"}}]}}],...}

data: {"usageMetadata":{"promptTokenCount":8,"candidatesTokenCount":1120,"totalTokenCount":1392,"trafficType":"ON_DEMAND","promptTokensDetails":[{"modality":"TEXT","tokenCount":8}],"candidatesTokensDetails":[{"modality":"IMAGE","tokenCount":1120}],"thoughtsTokenCount":264}}
```

<Note>
  流式响应中图片字段名为 `inlineData`（驼峰），而请求体中字段名为 `inline_data`（下划线）。这是原生 Gemini API 的行为。
</Note>

## 文生图示例

<CodeGroup>
  ```bash cURL theme={null}
  curl "https://www.anyfast.ai/v1beta/models/gemini-3-pro-image:streamGenerateContent?key=YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "contents": [
        {
          "role": "user",
          "parts": [
            { "text": "生成一张山间日落的图片" }
          ]
        }
      ],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {
          "aspectRatio": "16:9",
          "imageSize": "1K"
        }
      }
    }'
  ```

  ```python Python theme={null}
  import requests, base64, json

  url = "https://www.anyfast.ai/v1beta/models/gemini-3-pro-image:streamGenerateContent"
  params = {"key": "YOUR_API_KEY"}
  data = {
      "contents": [
          {
              "role": "user",
              "parts": [{"text": "生成一张山间日落的图片"}]
          }
      ],
      "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"],
          "imageConfig": {"aspectRatio": "16:9", "imageSize": "1K"}
      }
  }

  response = requests.post(url, params=params, json=data, stream=True)

  for line in response.iter_lines():
      if not line:
          continue
      decoded = line.decode("utf-8")
      if not decoded.startswith("data:"):
          continue
      chunk = json.loads(decoded[len("data:"):].strip())

      candidates = chunk.get("candidates", [])
      if not candidates:
          continue
      parts = candidates[0].get("content", {}).get("parts", [])
      for part in parts:
          # 跳过思考 chunk
          if part.get("thought"):
              continue
          # 保存图片 chunk
          if "inlineData" in part:
              img_bytes = base64.b64decode(part["inlineData"]["data"])
              with open("output.png", "wb") as f:
                  f.write(img_bytes)
              print("图片已保存至 output.png")
  ```
</CodeGroup>

## 图片编辑示例（传入参考图）

在同一个 `parts` 数组中同时传入 `text` 指令和 `inline_data` 参考图。

<CodeGroup>
  ```bash cURL theme={null}
  # 先将图片转为 base64：
  # BASE64=$(base64 -i your_photo.jpg)
  #
  # 然后发送请求：
  curl "https://www.anyfast.ai/v1beta/models/gemini-3-pro-image:streamGenerateContent?key=YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "contents": [
        {
          "role": "user",
          "parts": [
            {
              "text": "这是我的一张照片，请在我旁边加一只羊驼"
            },
            {
              "inline_data": {
                "mime_type": "image/jpeg",
                "data": "<YOUR_BASE64_ENCODED_IMAGE>"
              }
            }
          ]
        }
      ],
      "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {
          "aspectRatio": "1:1",
          "imageSize": "1K"
        }
      }
    }'
  ```

  ```python Python theme={null}
  import requests, base64, json

  # 读取并编码参考图片
  with open("your_photo.jpg", "rb") as f:
      image_b64 = base64.b64encode(f.read()).decode("utf-8")

  url = "https://www.anyfast.ai/v1beta/models/gemini-3-pro-image:streamGenerateContent"
  params = {"key": "YOUR_API_KEY"}
  data = {
      "contents": [
          {
              "role": "user",
              "parts": [
                  {
                      "text": "这是我的一张照片，请在我旁边加一只羊驼"
                  },
                  {
                      "inline_data": {
                          "mime_type": "image/jpeg",
                          "data": image_b64
                      }
                  }
              ]
          }
      ],
      "generationConfig": {
          "responseModalities": ["TEXT", "IMAGE"],
          "imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}
      }
  }

  response = requests.post(url, params=params, json=data, stream=True)

  for line in response.iter_lines():
      if not line:
          continue
      decoded = line.decode("utf-8")
      if not decoded.startswith("data:"):
          continue
      chunk = json.loads(decoded[len("data:"):].strip())

      candidates = chunk.get("candidates", [])
      if not candidates:
          continue
      parts = candidates[0].get("content", {}).get("parts", [])
      for part in parts:
          if part.get("thought"):
              continue
          if "inlineData" in part:
              img_bytes = base64.b64decode(part["inlineData"]["data"])
              with open("output.png", "wb") as f:
                  f.write(img_bytes)
              print("图片已保存至 output.png")
  ```
</CodeGroup>

## 参数说明

| 参数                                         | 类型     | 必填 | 说明                                          |
| ------------------------------------------ | ------ | -- | ------------------------------------------- |
| `key`                                      | string | 是  | API 密钥（查询参数）                                |
| `alt`                                      | string | 否  | 设为 `sse` 可显式开启 SSE 模式（可选，流式为默认行为）           |
| `contents[].parts[].text`                  | string | 是  | 文字提示或指令                                     |
| `contents[].parts[].inline_data.mime_type` | string | 否  | 参考图类型：`image/jpeg`、`image/png`、`image/webp` |
| `contents[].parts[].inline_data.data`      | string | 否  | Base64 编码的参考图数据                             |
| `generationConfig.responseModalities`      | array  | 是  | `["IMAGE"]` 或 `["TEXT", "IMAGE"]`           |
| `generationConfig.imageConfig.aspectRatio` | string | 否  | `1:1` / `4:3` / `3:4` / `16:9` / `9:16`     |
| `generationConfig.imageConfig.imageSize`   | string | 否  | `1K` / `2K` / `4K`（默认 `1K`）                 |

<Card title="API 参考" icon="code" href="/zh/api-reference/model-api/google/gemini-3-pro-image-stream">
  查看 Gemini 3 Pro Image (Stream) 的交互式 API Playground。
</Card>

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