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

文生圖

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.

模型對比

Nano Banana 2 Lite Text-to-Image 由 Google 開發。Atlas Cloud（由 Atlas Cloud AI LLC 營運）僅提供接取服務，並不擁有該模型。所有商標均歸其各自所有者所有。

輸入

Requests that fail Google's content review are still partially billed by Google, the model provider.

提示詞 *

寬高比

Thinking level

解析度

輸出

閒置

生成的圖片將在這裡顯示

設定參數後點擊執行開始生成

每次執行將花費 $0.04。$10 可執行約 250 次。

你可以繼續：

圖生影片圖生圖

參數

程式碼範例
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()

安裝

安裝所需的相依套件。

pip install requests

驗證

所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。

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

HTTP 標頭

import os

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

保護好您的 API Key

切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使用環境變數或後端代理。

提交請求

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())

提交請求

提交一個非同步生成請求。API 會傳回一個 prediction ID，您可以用它來檢查狀態與取得結果。

POST/api/v1/model/generateImage

請求主體

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']}")

回應

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

檢查狀態

輪詢 prediction 端點以檢查請求的當前狀態。

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

輪詢範例

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)

狀態值

processing請求仍在處理中。

completed生成完成，輸出可用。

succeeded生成成功，輸出可用。

failed生成失敗，請檢查 error 欄位。

完成回應

{
  "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"
  }
}

上傳檔案

將檔案上傳到 Atlas Cloud 儲存空間，取得可在 API 請求中使用的 URL。使用 multipart/form-data 上傳。

POST/api/v1/model/uploadMedia

上傳範例

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}")

回應

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

Input Schema

以下參數在請求主體中被接受。

總計: 7必填: 2選填: 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

範例請求主體

{
  "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 傳回包含生成輸出 URL 的 prediction 回應。

codeinteger

HTTP status code of the response.

messagestring

Human-readable message; non-empty on failure.

dataobject

範例回應

{
  "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 將 400+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API Key

從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。

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

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案用於圖片編輯和圖生影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 400+ AI 模型連接。支援任何相容 MCP 的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

npx -y atlascloud-mcp

設定

將以下設定新增到您的 IDE 的 MCP 設定檔中。

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

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 400+ 可用 AI 模型。

atlas_quick_generate一步式內容創建，自動選擇最佳模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

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"
                  }
                }
              },
              "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 友善的提示詞範本

# 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.


    - **`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": ""
    },
    "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)

A celestial maiden with snow-white hair embraced by golden-yellow parrots, serene expression, birds whispering around her, delicate ivory lace gown, enchanted fairytale atmosphere, golden morning light filtering through mist, subtle feather movement, flowing hair, cinematic dolly-in shot, dreamy bokeh, soft focus, emotional storytelling, fantasy realism, Denis Villeneuve-inspired cinematography, volumetric sunlight, ultra detailed, magical realism, masterpiece

載入中...

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

輸入

輸出

參數

程式碼範例

安裝

驗證

HTTP 標頭

提交請求

提交請求

請求主體

回應

檢查狀態

輪詢範例

狀態值

完成回應

上傳檔案

上傳範例

回應

Input Schema

範例請求主體

Output Schema

範例回應

Atlas Cloud Skills

支援的客戶端

安裝

設定 API Key

功能

MCP Server

支援的客戶端

安裝

設定

可用工具

API Schema

LLM 友善的提示詞範本

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 Lite Edit Developer

Nano Banana 2 Lite Text-to-Image Developer

Nano Banana 2 Lite Edit

Nano Banana 2 Reference to Image

Nano Banana 2 Reference to Image Developer

Nano Banana 2 Text-to-Image Developer

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 Pro Text-to-image Developer

Nano Banana Text-to-image Developer

Nano Banana Pro Edit Developer

一個 API，暢享全模態 AI。

輸入

輸出

參數

程式碼範例

安裝

驗證

HTTP 標頭

提交請求

提交請求

請求主體

回應

檢查狀態

輪詢範例

狀態值

完成回應

上傳檔案