google/nano-banana-2-lite/text-to-image

文生图

Nano Banana 2 Lite Text-to-Image API by Google

google/nano-banana-2-lite/text-to-image

Text-to-image

Nano banana lite is the efficiency-focused model in the image generation family. Sub-2 second latency with cost-effective generation and editing, fast multi-turn local edits, and 14 supported aspect ratios.

输入

提示词 *

宽高比

Thinking level

分辨率

输出

空闲

生成的图片将在这里显示

配置参数后点击运行开始生成

每次运行将花费 $0.04。$10 可运行约 250 次。

你可以继续：

图生视频图生图

参数

代码示例
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "google/nano-banana-2-lite/text-to-image",  # Required. model name
    "aspect_ratio": "auto",  # The aspect ratio of the generated media
    "enable_base64_output": False,  # If enabled, the output will be encoded into a BASE64 string instead of a URL
    "enable_sync_mode": False,  # If set to true, the function will wait for the result to be generated and uploaded before returning the response
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The positive prompt for the generation
    "resolution": "1k",  # The resolution of the output image. options: 1k
    "thinking_level": "default",  # Controls the amount of internal reasoning the model performs before generating a response. options: default | high | minimal
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

安装

安装所需的依赖包。

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP 请求头

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

提交请求

提交一个异步生成请求。API 返回一个 prediction ID，您可以用它来检查状态和获取结果。

POST/api/v1/model/generateImage

请求体

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "google/nano-banana-2-lite/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

响应

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

检查状态

轮询 prediction 端点以检查请求的当前状态。

GET/api/v1/model/prediction/{prediction_id}

轮询示例

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

状态值

processing请求仍在处理中。

completed生成完成，输出可用。

succeeded生成成功，输出可用。

failed生成失败，请检查 error 字段。

完成响应

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

上传文件

将文件上传到 Atlas Cloud 存储，获取可在 API 请求中使用的 URL。使用 multipart/form-data 上传。

POST/api/v1/model/uploadMedia

上传示例

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

响应

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

以下参数在请求体中被接受。

总计: 7必填: 2可选: 5

modelstringrequired

model name

Default: "google/nano-banana-2-lite/text-to-image"

aspect_ratiostring

The aspect ratio of the generated media.

Default: "auto"

auto1:13:22:33:44:34:55:49:1616:921:94:11:48:11:8

enable_base64_outputboolean

If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Default: false

enable_sync_modeboolean

If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.

Default: false

promptstringrequired

The positive prompt for the generation.

resolutionstring

The resolution of the output image.

Default: "1k"

thinking_levelstring

Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.

Default: "default"

defaulthighminimal

请求体示例

{
  "model": "google/nano-banana-2-lite/text-to-image",
  "aspect_ratio": "auto",
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "prompt": "A beautiful landscape",
  "resolution": "1k",
  "thinking_level": "default"
}

Output Schema

API 返回包含生成输出 URL 的 prediction 响应。

codeinteger

HTTP status code of the response.

messagestring

Human-readable message; non-empty on failure.

dataobject

响应示例

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills 将 400+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

export ATLASCLOUD_API_KEY="your-api-key-here"

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 400+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 400+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "google/nano-banana-2-lite/text-to-image"
          },
          "aspect_ratio": {
            "description": "The aspect ratio of the generated media.",
            "enum": [
              "auto",
              "1:1",
              "3:2",
              "2:3",
              "3:4",
              "4:3",
              "4:5",
              "5:4",
              "9:16",
              "16:9",
              "21:9",
              "4:1",
              "1:4",
              "8:1",
              "1:8"
            ],
            "default": "auto",
            "type": "string",
            "x-placeholder": "Select aspect ratio"
          },
          "enable_base64_output": {
            "default": false,
            "description": "If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.",
            "disabled": true,
            "type": "boolean",
            "x-advanced": true
          },
          "enable_sync_mode": {
            "default": false,
            "description": "If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.",
            "disabled": true,
            "type": "boolean",
            "x-advanced": true
          },
          "prompt": {
            "description": "The positive prompt for the generation.",
            "type": "string"
          },
          "resolution": {
            "default": "1k",
            "description": "The resolution of the output image.",
            "enum": [
              "1k"
            ],
            "type": "string",
            "x-advanced": true
          },
          "thinking_level": {
            "description": "Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.",
            "default": "default",
            "enum": [
              "default",
              "high",
              "minimal"
            ],
            "type": "string"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "aspect_ratio",
          "thinking_level",
          "resolution",
          "enable_sync_mode",
          "enable_base64_output"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "code": {
            "description": "HTTP status code of the response.",
            "type": "integer"
          },
          "message": {
            "description": "Human-readable message; non-empty on failure.",
            "type": "string"
          },
          "data": {
            "type": "object",
            "properties": {
              "id": {
                "description": "Unique identifier for the prediction.",
                "type": "string"
              },
              "model": {
                "description": "Model ID used for the prediction.",
                "type": "string"
              },
              "outputs": {
                "description": "Array of URLs to the generated content. Null when status is not completed.",
                "type": "array",
                "items": {
                  "type": "string"
                },
                "nullable": true
              },
              "urls": {
                "description": "Object containing related API endpoints.",
                "type": "object",
                "properties": {
                  "get": {
                    "description": "URL to poll for the prediction result.",
                    "type": "string",
                    "format": "uri"
                  }
                }
              },
              "has_nsfw_contents": {
                "description": "Array of boolean values indicating NSFW detection for each output. Null if not applicable.",
                "type": "array",
                "items": {
                  "type": "boolean"
                },
                "nullable": true
              },
              "status": {
                "description": "Status of the task: created, processing, completed, timeout, or failed.",
                "type": "string"
              },
              "created_at": {
                "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
                "format": "date-time",
                "type": "string"
              },
              "error": {
                "description": "Error message if the task failed, empty string otherwise.",
                "type": "string"
              },
              "error_code": {
                "description": "Error code if the task failed.",
                "type": "integer"
              },
              "executionTime": {
                "description": "Total execution time in milliseconds.",
                "type": "number"
              },
              "timings": {
                "description": "Detailed timing breakdown.",
                "type": "object",
                "properties": {
                  "inference": {
                    "description": "Inference time in milliseconds.",
                    "type": "number"
                  }
                }
              },
              "stt_result": {
                "description": "The speech-to-text transcription result.",
                "type": "object",
                "allOf": [
                  {
                    "$ref": "#/components/schemas/TranscriptionResult"
                  },
                  {
                    "type": "object",
                    "properties": {
                      "channels": {
                        "description": "Per-channel transcription results. Populated when multichannel is enabled; each element corresponds to one audio channel and mirrors the transcription result with an additional channel index.",
                        "type": "array",
                        "items": {
                          "$ref": "#/components/schemas/ChannelTranscriptionResult"
                        }
                      }
                    }
                  }
                ]
              }
            }
          }
        }
      },
      "TranscriptionWord": {
        "description": "A word-level transcription segment with timestamps.",
        "type": "object",
        "properties": {
          "text": {
            "description": "The transcribed word text.",
            "type": "string"
          },
          "start": {
            "description": "Start time of the word in seconds.",
            "type": "number"
          },
          "end": {
            "description": "End time of the word in seconds.",
            "type": "number"
          },
          "type": {
            "description": "Segment type (e.g. \"word\").",
            "type": "string"
          },
          "speaker_id": {
            "description": "Identifier of the detected speaker. Populated when diarize is enabled.",
            "type": "string"
          }
        }
      },
      "TranscriptionResult": {
        "description": "The speech-to-text transcription result.",
        "type": "object",
        "properties": {
          "text": {
            "description": "The full transcribed text.",
            "type": "string"
          },
          "language": {
            "description": "Detected language name.",
            "type": "string"
          },
          "language_code": {
            "description": "Detected language code (ISO 639-3).",
            "type": "string"
          },
          "duration": {
            "description": "Duration of the audio in seconds.",
            "type": "number"
          },
          "language_probability": {
            "description": "Confidence probability of the detected language, between 0 and 1.",
            "type": "number"
          },
          "words": {
            "description": "Array of word-level transcription segments with timestamps.",
            "type": "array",
            "items": {
              "$ref": "#/components/schemas/TranscriptionWord"
            }
          }
        }
      },
      "ChannelTranscriptionResult": {
        "description": "Transcription result for a single audio channel. Mirrors TranscriptionResult with an additional channel index.",
        "type": "object",
        "allOf": [
          {
            "$ref": "#/components/schemas/TranscriptionResult"
          },
          {
            "type": "object",
            "properties": {
              "index": {
                "description": "Zero-based index of the audio channel this result corresponds to.",
                "type": "integer"
              }
            }
          }
        ]
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM 友好的提示词模板

# google/nano-banana-2-lite/text-to-image

> Nano banana lite is the efficiency-focused model in the image generation family. Sub-2 second latency with cost-effective generation and editing, fast multi-turn local edits, and 14 supported aspect ratios.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `google/nano-banana-2-lite/text-to-image`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"google/nano-banana-2-lite/text-to-image"`

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the generated media.
  - Default: `"auto"`
  - Options: "auto", "1:1", "3:2", "2:3", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9", "4:1", "1:4", "8:1", "1:8"

- **`thinking_level`** (`string`, _optional_):
  Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency.
  - Default: `"default"`
  - Options: "default", "high", "minimal"

- **`resolution`** (`string`, _optional_):
  The resolution of the output image.
  - Default: `"1k"`
  - Options: "1k"

- **`enable_sync_mode`** (`boolean`, _optional_):
  If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
  - Default: `false`

- **`enable_base64_output`** (`boolean`, _optional_):
  If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "google/nano-banana-2-lite/text-to-image",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "google/nano-banana-2-lite/text-to-image",
  "prompt": "",
  "aspect_ratio": "auto",
  "thinking_level": "default",
  "resolution": "1k",
  "enable_sync_mode": false,
  "enable_base64_output": false
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):
  HTTP status code of the response.

- **`message`** (`string`, _optional_):
  Human-readable message; non-empty on failure.

- **`data`** (`object`, _optional_):
  - Properties:
    - **`id`** (`string`, _optional_):
      Unique identifier for the prediction.

    - **`model`** (`string`, _optional_):
      Model ID used for the prediction.

    - **`outputs`** (`array[string]`, _optional_):
      Array of URLs to the generated content. Null when status is not completed.

    - **`urls`** (`object`, _optional_):
      Object containing related API endpoints.
      - Properties:
        - **`get`** (`string`, _optional_):
          URL to poll for the prediction result.


    - **`has_nsfw_contents`** (`array[boolean]`, _optional_):
      Array of boolean values indicating NSFW detection for each output. Null if not applicable.

    - **`status`** (`string`, _optional_):
      Status of the task: created, processing, completed, timeout, or failed.

    - **`created_at`** (`string`, _optional_):
      ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

    - **`error`** (`string`, _optional_):
      Error message if the task failed, empty string otherwise.

    - **`error_code`** (`integer`, _optional_):
      Error code if the task failed.

    - **`executionTime`** (`number`, _optional_):
      Total execution time in milliseconds.

    - **`timings`** (`object`, _optional_):
      Detailed timing breakdown.
      - Properties:
        - **`inference`** (`number`, _optional_):
          Inference time in milliseconds.


    - **`stt_result`** (`object`, _optional_):
      The speech-to-text transcription result.




**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": {
    "id": "",
    "model": "",
    "outputs": [
      ""
    ],
    "urls": {
      "get": ""
    },
    "has_nsfw_contents": [],
    "status": "",
    "created_at": "",
    "error": "",
    "error_code": 0,
    "executionTime": 0,
    "timings": {
      "inference": 0
    },
    "stt_result": {}
  }
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/nano-banana-2-lite/text-to-image",
  "prompt": "",
  "aspect_ratio": "auto",
  "thinking_level": "default",
  "resolution": "1k",
  "enable_sync_mode": false,
  "enable_base64_output": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/google/nano-banana-2-lite/text-to-image)

暂无可用示例

加载中...

Google Nano Banana 2 Lite — Text-to-Image

Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image, gemini-3.1-flash-lite-image) is Google's fastest and most cost-efficient image model in the Nano Banana family. This variant turns a natural-language text prompt into a high-quality image in as little as 4 seconds, making it purpose-built for rapid iteration, high-throughput pipelines, and cost-sensitive workflows — from blank page to finished layout in a single, near-instant step.

It replaces the original Nano Banana (Gemini 2.5 Flash Image) with improved visual quality, better world knowledge, stronger character consistency, and more legible in-image text — all while being faster and cheaper.

🌟 Why it stands out

Lightning-fast generation — Produces images in roughly 4 seconds, delivering the lowest latency in its class so you can iterate faster than you can rethink the prompt.
Reliable prompt adherence — Faithfully interprets subjects, backgrounds, and spatial relationships to build contextually correct compositions.
Strong character consistency — Keeps character identities and object fidelity stable across multiple generations, ideal for storyboards and series content.
Legible in-image text — Renders readable text and quick localizations directly into generated images for posters, mockups, and social assets.
World knowledge — Drafts accurate contextual scenes, rough data visualizations, and location-specific mockups grounded in real-world understanding.
Most cost-efficient tier — The budget-friendly member of the Nano Banana family, engineered for high-volume generation at scale.

⚙️ Capabilities

Input: text prompt (natural language)
Output: high-quality image (JPEG/PNG/WEBP), delivered as a URL (or BASE64 via the API)
Resolution: 1k
Aspect ratios: auto, 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 4:1, 1:4, 8:1, 1:8
Thinking level: default, high, or minimal — controls how much internal reasoning the model performs before generating. Higher levels can improve quality on complex prompts at the cost of latency; minimal maximizes speed.
Compatible with descriptive prompts such as:
- "A golden retriever playing in a field of sunflowers at sunset."
- "A futuristic city skyline with neon reflections on wet streets, cinematic lighting."
- "A minimalist product poster for a coffee brand with the headline 'Freshly Roasted'."

💰 Pricing

Pricing is a simple flat fee per generated 1k image. There is a single SKU and the total scales linearly with the number of images returned.

SKU	Description	Unit Price
`sku_1k`	1k image	$0.04

Pricing Formula

cost = sku_1k × num_images

num_images — number of images generated by the request (default 1).

Examples

Images	Calculation	Total
1	$0.04 × 1	$0.04
4	$0.04 × 4	$0.16
10	$0.04 × 10	$0.40

For the exact pricing configuration, see models/google/banana/price/google-nano-banana-2-lite-text-to-image.json.

💡 Best Use Cases

Rapid ideation & A/B testing — Fire off many variations and quickly compare directions.
Social media & marketing — Generate on-brand visuals and ad variations at speed.
Storyboarding & concept art — Build moodboards and sequential frames with consistent characters.
Slide decks & web pages — Produce supporting imagery for presentations and landing pages on the fly.
High-volume, real-time apps — Power interactive image generation for large user bases and autonomous workflows.

🔒 Content Authenticity

Generated images include C2PA content credentials and an imperceptible SynthID watermark by default, so outputs can be identified as AI-generated.

⚠️ Limitations

Optimized for speed and cost, this model may struggle with small faces, accurate spelling, and very fine details. Data-driven content can occasionally be misinterpreted or factually incorrect, and advanced edits or major lighting transformations are better handled by higher-tier Nano Banana models.

📝 Notes

Please ensure your prompts comply with Google's Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.

Nano Banana 2 Lite — Edit — The same model driven by input images plus a text prompt for editing.
Nano Banana Pro — Text-to-Image — Higher-fidelity tier for premium production.

Nano Banana 2 Lite Text-to-Image API by Google

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LLM 友好的提示词模板

Google Nano Banana 2 Lite — Text-to-Image

🌟 Why it stands out

⚙️ Capabilities

💰 Pricing

Pricing Formula

Examples

💡 Best Use Cases

🔒 Content Authenticity

⚠️ Limitations

📝 Notes

🔗 Related Models

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Nano Banana 2 Text-to-Image

Nano Banana 2 Edit Developer

Nano Banana 2 Edit

Nano Banana Pro Text-to-image Ultra

Nano Banana Pro Edit Ultra

Nano Banana Pro Text-to-image

Nano Banana Pro Edit

Nano Banana Text-to-image Developer

Nano Banana Pro Text-to-image Developer

Nano Banana Edit Developer

一个 API，畅享全模态 AI。

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输入

输出

参数

代码示例

安装

认证

HTTP 请求头

提交请求

提交请求

请求体

响应

检查状态

轮询示例

状态值

完成响应