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

テキストから画像

Nano Banana 2 Text-to-Image API by Google

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

Text-to-image

Google's lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts.

入力

プロンプト *

アスペクト比

解像度

Thinking level

Media resolution

Output format

Enable web search

Enable image search

出力

待機中

生成された画像がここに表示されます

設定を構成して「実行」をクリックして開始

各実行には$0.08かかります。$10で約125回実行できます。

次にできること：

画像から動画画像から画像

パラメータ

コード例
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/text-to-image",  # Required. model name
    "aspect_ratio": "example_value",  # 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
    "enable_web_search": False,  # If enabled, the model will use web search to ground the generation with real-time information
    "enable_image_search": False,  # If enabled, the model will use image search to ground the generation with real-time information
    "output_format": "default",  # The format of the output image. options: default | png | jpeg
    "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 | 2k | 4k
    "media_resolution": "default",  # Controls how input media is processed. options: default | low | medium | high
    "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()

インストール

お使いの言語に必要なパッケージをインストールしてください。

pip install requests

認証

すべての API リクエストには API キーによる認証が必要です。API キーは Atlas Cloud ダッシュボードから取得できます。

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

HTTP ヘッダー

import os

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

API キーを安全に保管してください

API キーをクライアントサイドのコードや公開リポジトリに公開しないでください。代わりに環境変数またはバックエンドプロキシを使用してください。

リクエストを送信

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())

リクエストを送信

非同期生成リクエストを送信します。API は予測 ID を返し、それを使用してステータスの確認や結果の取得ができます。

POST/api/v1/model/generateImage

リクエストボディ

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/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']}")

レスポンス

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

ステータスを確認

予測エンドポイントをポーリングして、リクエストの現在のステータスを確認します。

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

ポーリング例

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)

ステータス値

processingリクエストはまだ処理中です。

completed生成が完了しました。出力が利用可能です。

succeeded生成が成功しました。出力が利用可能です。

failed生成に失敗しました。エラーフィールドを確認してください。

完了レスポンス

{
  "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"
  }
}

ファイルをアップロード

Atlas Cloud ストレージにファイルをアップロードし、API リクエストで使用できる URL を取得します。multipart/form-data を使用してアップロードします。

POST/api/v1/model/uploadMedia

アップロード例

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}")

レスポンス

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

入力 Schema

以下のパラメータがリクエストボディで使用できます。

合計: 11必須: 2任意: 9

modelstringrequired

model name

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

aspect_ratiostring

The aspect ratio of the generated media.

1:13:22:33:44:34:55:49:1616:921:9

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

enable_web_searchboolean

If enabled, the model will use web search to ground the generation with real-time information.

Default: false

enable_image_searchboolean

If enabled, the model will use image search to ground the generation with real-time information.

Default: false

output_formatstring

The format of the output image.

Default: "default"

defaultpngjpeg

promptstringrequired

The positive prompt for the generation.

resolutionstring

The resolution of the output image.

Default: "1k"

1k2k4k

media_resolutionstring

Controls how input media is processed. LOW reduces tokens per image/video, possibly losing detail but allowing longer videos in context. Supported values: HIGH, MEDIUM, LOW.

Default: "default"

defaultlowmediumhigh

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

リクエストボディの例

{
  "model": "google/nano-banana-2/text-to-image",
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "enable_web_search": false,
  "enable_image_search": false,
  "output_format": "default",
  "prompt": "A beautiful landscape",
  "resolution": "1k",
  "media_resolution": "default",
  "thinking_level": "default"
}

出力 Schema

API は生成された出力 URL を含む予測レスポンスを返します。

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

statusstring

Status of the task: created, processing, completed, or failed.

レスポンス例

{
  "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 は 400 以上の AI モデルを AI コーディングアシスタントに直接統合します。ワンコマンドでインストールし、自然言語で画像・動画生成や LLM との対話が可能です。

対応クライアント

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 対応クライアント

インストール

npx skills add AtlasCloudAI/atlas-cloud-skills

API キーの設定

Atlas Cloud ダッシュボードから API キーを取得し、環境変数として設定してください。

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

機能

インストール後、AI アシスタントで自然言語を使用してすべての Atlas Cloud モデルにアクセスできます。

画像生成Nano Banana 2、Z-Image などのモデルで画像を生成します。

動画作成Kling、Vidu、Veo などでテキストや画像から動画を作成します。

LLM チャットQwen、DeepSeek などの大規模言語モデルと対話します。

メディアアップロード画像編集や画像から動画へのワークフロー用にローカルファイルをアップロードします。

詳細を見る

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server は Model Context Protocol を通じて IDE と 400 以上の AI モデルを接続します。MCP 対応のあらゆるクライアントで動作します。

対応クライアント

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 対応クライアント

インストール

npx -y atlascloud-mcp

設定

以下の設定を IDE の MCP 設定ファイルに追加してください。

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

利用可能なツール

atlas_generate_imageテキストプロンプトから画像を生成します。

atlas_generate_videoテキストや画像から動画を作成します。

atlas_chat大規模言語モデルと対話します。

atlas_list_models400 以上の利用可能な AI モデルを閲覧します。

atlas_quick_generate最適なモデルを自動選択し、ワンステップでコンテンツを作成。

atlas_upload_mediaAPI ワークフロー用にローカルファイルをアップロードします。

詳細を見る

github.com/AtlasCloudAI/mcp-server

APIスキーマ

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "google/nano-banana-2/text-to-image"
          },
          "aspect_ratio": {
            "description": "The aspect ratio of the generated media.",
            "enum": [
              "1:1",
              "3:2",
              "2:3",
              "3:4",
              "4:3",
              "4:5",
              "5:4",
              "9:16",
              "16:9",
              "21:9"
            ],
            "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"
          },
          "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"
          },
          "enable_web_search": {
            "default": false,
            "description": "If enabled, the model will use web search to ground the generation with real-time information.",
            "type": "boolean"
          },
          "enable_image_search": {
            "default": false,
            "description": "If enabled, the model will use image search to ground the generation with real-time information.",
            "type": "boolean"
          },
          "output_format": {
            "default": "default",
            "description": "The format of the output image.",
            "enum": [
              "default",
              "png",
              "jpeg"
            ],
            "type": "string"
          },
          "prompt": {
            "description": "The positive prompt for the generation.",
            "type": "string"
          },
          "resolution": {
            "default": "1k",
            "description": "The resolution of the output image.",
            "enum": [
              "1k",
              "2k",
              "4k"
            ],
            "type": "string"
          },
          "media_resolution": {
            "default": "default",
            "description": "Controls how input media is processed. LOW reduces tokens per image/video, possibly losing detail but allowing longer videos in context. Supported values: HIGH, MEDIUM, LOW.",
            "enum": [
              "default",
              "low",
              "medium",
              "high"
            ],
            "type": "string"
          },
          "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"
        ],
        "seed": {
          "title": "Seed",
          "type": "integer"
        },
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "aspect_ratio",
          "resolution",
          "thinking_level",
          "media_resolution",
          "output_format",
          "enable_web_search",
          "enable_image_search",
          "enable_sync_mode",
          "enable_base64_output"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction, the ID of the prediction to get.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated content (empty when status is not completed).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "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フレンドリーなプロンプトテンプレート

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

> Google's lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts.


## 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/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/text-to-image"`

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

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

- **`resolution`** (`string`, _optional_):
  The resolution of the output image.
  - Default: `"1k"`
  - Options: "1k", "2k", "4k"

- **`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"

- **`media_resolution`** (`string`, _optional_):
  Controls how input media is processed. LOW reduces tokens per image/video, possibly losing detail but allowing longer videos in context. Supported values: HIGH, MEDIUM, LOW.
  - Default: `"default"`
  - Options: "default", "low", "medium", "high"

- **`output_format`** (`string`, _optional_):
  The format of the output image.
  - Default: `"default"`
  - Options: "default", "png", "jpeg"

- **`enable_web_search`** (`boolean`, _optional_):
  If enabled, the model will use web search to ground the generation with real-time information.
  - Default: `false`

- **`enable_image_search`** (`boolean`, _optional_):
  If enabled, the model will use image search to ground the generation with real-time information.
  - Default: `false`

- **`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/text-to-image",
  "prompt": ""
}
```


**Full Example**:

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


### Output Schema

The API returns the following output format:


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

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

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

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

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

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## 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/text-to-image",
  "prompt": "",
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "thinking_level": "default",
  "media_resolution": "default",
  "output_format": "default",
  "enable_web_search": false,
  "enable_image_search": false,
  "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/text-to-image)

cyberpunk megacity at night, heavy rain, neon lights reflecting on wet streets, dense futuristic skyscrapers, giant holographic advertisements, flying vehicles in the sky, thick fog and atmospheric haze, moody cinematic lighting, dark dystopian atmosphere, ultra detailed, cinematic composition, 35mm film still, volumetric lighting, cyberpunk aesthetic

a vast open wilderness where colossal animal-like machines roam across green valleys and rivers, towering mountains and ancient futuristic ruins in the distance, a lone huntress observing from a cliff edge, wind sweeping across tall grass, sunlight illuminating metallic bodies of the machines, cinematic wide angle composition, monumental environmental scale, vibrant yet mysterious atmosphere, ultra realistic, horizon aesthetic

a lone futuristic courier walking through a vast desolate landscape during a heavy timefall rainstorm, carrying tall stacks of cargo containers on his back, dark clouds covering the sky, rain falling in long streaks and splashing on the ground, wet rocky terrain and muddy paths reflecting dim light, the courier wearing a high-tech delivery suit with cables and sensors, a transparent protective hood over his head, mist drifting across the valley, distant mountains fading into the rain, cinematic ultra wide shot, epic environmental scale, melancholic and tense atmosphere, ultra realistic, post-apocalyptic sci-fi world, death stranding style

読み込み中...

次世代画像生成

最大 4K 解像度の出力（512px / 1K / 2K / 4K の 4 段階）
21:9、1:4、8:1 など 10 種類以上のアスペクト比に対応
画像内の正確で読みやすいテキストレンダリング
Flash の速度で Pro 級に迫る画質（約 95%）

インテリジェント編集と一貫性

シーンをまたいで最大 5 キャラクターの一貫性を維持
単一のワークフローで最大 14 オブジェクトの忠実度を保持
自然言語による的確な編集（削除、置換、ポーズ変更）
マルチ画像ブレンディングとシームレスな合成

Nano Banana 2 の新機能

Pro より 3〜5 倍高速

Gemini 3.1 Flash アーキテクチャをベースとする Nano Banana 2 は、標準的な画像をわずか 4〜8 秒で生成します。Nano Banana Pro の 10〜20 秒と比べて大幅に高速です。

画像検索によるグラウンディング

NB2 の目玉機能 — 生成時に Google 検索を通じて現実世界の参考画像を取得し、ランドマーク・著名人・ブランドロゴの生成精度を大幅に高めます。

正確なテキストレンダリング

マーケティング素材、グリーティングカード、ローカライズコンテンツ向けに、正確で読みやすいテキストを生成します。画像内のテキストをそのまま翻訳・ローカライズすることも可能です。

マルチキャラクター一貫性

シーンをまたいで最大 5 キャラクターと 14 オブジェクトの視覚的な一貫性を維持 — ストーリーボード、漫画制作、マーケティングキャンペーンに最適です。

Text Rendering

Marketing Mockup with Text

Generate marketing visuals with accurate, legible text — one of NB2's standout improvements

Prompt

A minimalist coffee shop promotional poster with the text 'MORNING BREW — Fresh Roasted Daily' in elegant serif font, warm earth tones, steam rising from a ceramic cup, clean layout with plenty of whitespace

Character Consistency

Multi-Scene Character

Maintain character consistency across multiple scenes — supports up to 5 characters per workflow

Prompt

A young woman with short red hair and freckles, wearing a green jacket, standing in a rainy Tokyo street at night with neon reflections on wet pavement, cinematic lighting, photorealistic

Photo to Action Figure

Person to Action Figure

Transform people from photos into collectible action figures with custom packaging

Prompt

Transform the person in the photo into an action figure, styled after [CHARACTER_NAME] from [SOURCE / CONTEXT]. Next to the figure, display the accessories including [ITEM_1], [ITEM_2], and [ITEM_3]. On the top of the toy box, write "[BOX_LABEL_TOP]", and underneath it, "[BOX_LABEL_BOTTOM]". Place the box in a [BACKGROUND_SETTING] environment.

Search Grounding

Real-World Reference Generation

Leverage Image Search Grounding to generate accurate real-world subjects like landmarks and brands

Prompt

A photorealistic aerial view of the Eiffel Tower at golden hour, with the Seine River winding through Paris below, warm sunset light casting long shadows, high detail, 4K resolution

Product Photography

Product Design Render

Create professional product photography with precise control over lighting and composition

Prompt

A frosted glass perfume bottle with a marble cap on a white marble surface, soft studio lighting from the left, subtle reflections, minimalist luxury aesthetic, product photography style

Style Transfer

Artistic Style Transformation

Apply diverse artistic styles while maintaining subject integrity

Prompt

Transform this photo into Studio Ghibli animation style, keeping the same composition and subjects, lush watercolor backgrounds, soft diffused lighting, whimsical atmosphere

4K Output

Ultra High Resolution Scene

Generate detailed scenes at up to 4K resolution with rich textures

Prompt

A cozy Japanese ramen shop interior at night, steam rising from bowls, warm amber lighting, detailed wooden counter with various condiments, a chef working in the background, 4K, ultra detailed

ユースケース

🎬

ストーリーボードと漫画

📸

プロダクト撮影

📊

マーケティングモックアップ

📱

ソーシャルメディアコンテンツ

🔤

テキストオーバーレイデザイン

👤

キャラクターデザイン

✨

写真編集とレタッチ

🎨

ブランドビジュアルコンテンツ

なぜNano Banana 2を選ぶのか？

⚡

Flashスピード

Nano Banana Pro より 3〜5 倍高速、標準的な生成時間はわずか 4〜8 秒

🎯

ほぼプロ品質

ほとんどのシナリオで Pro の約 95% の画質を実現

💰

コスト効率

コストは Nano Banana Pro の約半分 — 高品質な AI 画像生成をより身近に

技術仕様

アーキテクチャ：Gemini 3.1 Flash (GEMPIX2)

解像度サポート：512px から 4K まで（512px / 1K / 2K / 4K の 4 段階）

アスペクト比：1:1, 4:3, 3:4, 2:3, 3:2, 16:9, 9:16, 1:4, 4:1, 8:1, 21:9

一貫性：ワークフローあたり最大 5 キャラクター + 14 オブジェクト

コンテンツ安全性：SynthID透かし、C2PA標準対応

APIアクセス：Gemini API、Vertex AI、AI Studio、Gemini CLI

Nano Banana 2 を体験する

Flash の速度で Pro 級の画像生成 — キャラクターの一貫性、テキストレンダリング、4K 解像度対応で、美しいビジュアルを手軽に作成できます。

✨無料クレジットで開始

⚡即座にAPIアクセス

🌐セットアップ不要

Google Nano Banana 2 Text-to-Image

Nano Banana 2 Text-to-Image (Gemini 3.1 Flash Image) is Google’s lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts. It transforms words into expressive, realistic images with remarkable clarity, composition, and style diversity — all within seconds.

Why Choose This?

Fast generation Optimized for speed while maintaining high visual quality.
Multi-resolution support Output in 1K, 2K, or 4K resolution based on your needs.
Flexible aspect ratios Multiple options including 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9.
Cinematic quality Excels at photorealistic scenes with atmospheric lighting and anamorphic lens effects.
Prompt Enhancer Built-in tool to automatically improve your descriptions.
Format choice Export in PNG or JPEG format.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the desired image
aspect_ratio	No	Aspect ratio: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolution	No	Output resolution: 1k (default), 2k, 4k
output_format	No	Output format: png (default), jpeg

How to Use

Write your prompt — describe the image in detail, including style, lighting, and composition.
Choose aspect ratio (optional) — select a preset or leave empty for default.
Select resolution — choose 1K, 2K, or 4K based on your needs.
Choose output format — PNG for transparency support, JPEG for smaller file size.
Use Prompt Enhancer (optional) — click to automatically refine your description.
Run — submit and download your generated image.

Pricing

Resolution	Cost
1k	$0.08
2k	$0.12
4k	$0.16

Best Use Cases

Cinematic Imagery — Generate atmospheric scenes with anamorphic lens effects and film-like quality.
Character Portraits — Create detailed character illustrations with specific attributes.
Social Media Content — Produce eye-catching visuals for various platforms with flexible aspect ratios.
Marketing & Ads — Generate professional images for campaigns and promotions.
Concept Art — Visualize creative ideas with photorealistic rendering.

Pro Tips

Use cinematic language like "anamorphic lens look," "neon signs reflected in puddles," or "rainy Tokyo street at night" for atmospheric results.
Be specific about character attributes (hair color, clothing, accessories) for consistent results.
Match aspect ratio to your target platform: 9:16 for Stories/Reels, 16:9 for banners, 1:1 for feeds.
Try the Prompt Enhancer to automatically improve your descriptions.
2K offers the best value — same price as 1K with higher resolution.

Notes

Prompt is the only required field.
If aspect_ratio is not selected, the model uses a default ratio.
4K resolution costs 2× the standard rate.
Ensure your prompts comply with Google's Safety Guidelines.

Nano Banana Pro Text-to-Image — Pro tier with enhanced quality.
Nano Banana Pro Edit — Edit images with text instructions.
Nano Banana 2 Edit — Image editing capabilities.

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From

$0.14/画像