google/nano-banana/edit-developer

Open and Advanced Large-Scale Image Generative Models.

IMAGE-TO-IMAGENEW
Nano Banana Edit Developer
画像から画像
DEV

Open and Advanced Large-Scale Image Generative Models.

入力

パラメータ設定を読み込み中...

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$0.023かかります。$10で約434回実行できます。

次にできること:

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/edit-developer",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

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。

bash
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。

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

HTTP ヘッダー

python
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/edit-developer",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

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

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

レスポンス

{
  "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: 0必須: 0任意: 0

利用可能なパラメータはありません。

リクエストボディの例

json
{
  "model": "google/nano-banana/edit-developer"
}

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。

idstringrequired
Unique identifier for the prediction.
statusstringrequired
Current status of the prediction.
processingcompletedsucceededfailed
modelstringrequired
The model used for generation.
outputsarray[string]
Array of output URLs. Available when status is "completed".
errorstring
Error message if status is "failed".
metricsobject
Performance metrics.
predict_timenumber
Time taken for image generation in seconds.
created_atstringrequired
ISO 8601 timestamp when the prediction was created.
Format: date-time
completed_atstring
ISO 8601 timestamp when the prediction was completed.
Format: date-time

レスポンス例

json
{
  "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 300 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

bash
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。

bash
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。

MCP Server

Atlas Cloud MCP Server は Model Context Protocol を通じて IDE と 300 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

bash
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。

json
{
  "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_models300 以上の利用可能な AI モデルを閲覧します。
atlas_quick_generateモデル自動選択によるワンステップコンテンツ作成。
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。

APIスキーマ

スキーマが利用できません

リクエスト履歴を表示するにはログインしてください

モデルのリクエスト履歴にアクセスするにはログインが必要です。

ログイン

Seedance 1.5 Pro

ネイティブ音声・映像同期生成

音と映像を、ワンテイクで完全同期

ByteDanceの革新的なAIモデル。単一の統合プロセスから完璧に同期した音声と映像を同時生成。8言語以上でミリ秒精度のリップシンクを実現する、真のネイティブ音声・映像生成を体験してください。

Advanced Image Generation
  • Multi-image fusion technology
  • Character consistency across generations
  • Style-preserving transformations
  • High-resolution output up to 4K
Smart Editing Tools
  • Text-based intelligent editing
  • Object addition and removal
  • Background replacement
  • Style transfer and artistic effects

Prompt Examples & Templates

Explore curated prompt templates to unlock the full potential of Nano Banana AI. Click to copy any prompt and start creating immediately.

Photo to Character Figure
Transform to Figure

Photo to Character Figure

Transform any photo into a realistic character figure with packaging and display
Prompt

turn this photo into a character figure. Behind it, place a box with the character's image printed on it, and a computer showing the Blender modeling process on its screen. In front of the box, add a round plastic base with the character figure standing on it. set the scene indoors if possible

Anime to Cosplay
Anime to Real

Anime to Cosplay

Transform anime illustrations into realistic cosplay photography
Prompt

Generate a highly detailed photo of a girl cosplaying this illustration, at Comiket. Exactly replicate the same pose, body posture, hand gestures, facial expression, and camera framing as in the original illustration. Keep the same angle, perspective, and composition, without any deviation

Person to Action Figure
Photo to Action Figure

Person to Action Figure

Transform people from photos into collectible action figures with custom packaging
Prompt

Transform the 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. Visualize this in a highly realistic way with attention to fine details.

Person to Funko Pop Figure
Photo to Funko Pop

Person to Funko Pop Figure

Transform photos into Funko Pop style collectible figures with custom packaging
Prompt

Transform the person in the photo into the style of a Funko Pop figure packaging box, presented in an isometric perspective. Label the packaging with the title 'ZHOGUE'. Inside the box, showcase the figure based on the person in the photo, accompanied by their essential items (such as cosmetics, bags, or others). Next to the box, also display the actual figure itself outside of the packaging, rendered in a realistic and lifelike style.

Product Design to Photorealistic Render
Design to Reality

Product Design to Photorealistic Render

Transform product design sketches into photorealistic renders
Prompt

turn this illustration of a perfume into a realistic version, Frosted glass bottle with a marble cap

Transform to Q-Version Character
Face Reference Control

Transform to Q-Version Character

Create cartoon characters with face shape reference control
Prompt

Transform the person from image 1 into a Q-version character design based on the face shape from image 2

Building to 3D Architecture Model
Architecture to Model

Building to 3D Architecture Model

Convert architectural photos into detailed physical models
Prompt

convert this photo into a architecture model. Behind the model, there should be a cardboard box with an image of the architecture from the photo on it. There should also be a computer, with the content on the computer screen showing the Blender modeling process of the figurine. In front of the cardboard box, place a cardstock and put the architecture model from the photo I provided on it. I hope the PVC material can be clearly presented. It would be even better if the background is indoors.

Technical Highlights

Performance
Lightning-Fast Generation

Optimized for speed with generation times under 2 seconds for most tasks, making it perfect for real-time applications and rapid prototyping workflows.

Quality
Exceptional Output Quality

Leveraging Google's advanced AI architecture to produce highly detailed, photorealistic images with accurate lighting, textures, and compositions.

Innovation
Novel View Synthesis

Revolutionary 2D-to-3D conversion capabilities enabling creation of multiple viewpoints from a single image, opening new possibilities for content creation.

最適な用途

📸
Product Photography
🎨
Digital Art Creation
Photo Enhancement
📊
Marketing Visuals
👤
Character Design
👔
Virtual Try-On
📱
Social Media
🔄
Photo Restoration

Why Choose Nano Banana?

🚀
No Setup Required
Start creating immediately without complex configurations or installations
🎯
Precision Control
Fine-tune every aspect of your creation with intuitive text commands
🔄
Consistent Results
Maintain character and style consistency across multiple generations

技術仕様

Model Architecture:Google AI Studio Powered
Processing Speed:< 2 seconds average generation time
Resolution Support:Up to 4096x4096 pixels
Format Support:PNG, JPEG, WebP output formats
Multi-modal Input:Text, Image, and Combined prompts
API Integration:RESTful API with comprehensive documentation

ネイティブ音声・映像生成を体験

Seedance 1.5 Proの画期的なテクノロジーで動画コンテンツ制作を革新している世界中の映画制作者、広告主、クリエイターの仲間入りをしてください。

Free Credits to Start
Instant Access
🌐Works Everywhere

Google Nano-Banana Edit

Nano-Banana Edit is Google’s advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words. Built on Google’s cutting-edge computer vision and generative research, it combines precision, flexibility, and semantic awareness for professional-grade editing.

Difference to Nano Banana Edit: This model is cheaper and less stable than the version of Nano Banana Edit.

Try the New Version of Nano Banana!

🌟 Why it stands out

  • Natural Language Editing Modify images using simple text instructions — no masking, layering, or manual tools required.
  • Context-Aware Understanding Accurately interprets scene structure, spatial relationships, and object semantics for realistic results.
  • Style and Tone Preservation Keeps lighting, shadows, and texture consistent with the original image while applying changes seamlessly.
  • High Precision Control Excels at fine-grained edits such as color adjustments, object replacement, or composition shifts with minimal distortion.
  • Creative Versatility Suitable for concept art, photography, advertising design, and everyday content creation.

⚙️ How to use

  • Input: existing image + text prompt

  • Output: edited image (JPEG/PNG/WEBP)

  • Size: 1:1, 4:3, 16:9, 21:9, and so on.

  • Supports style transfer, relighting, background replacement, and object modification

  • Works with natural prompts like:

    • “Replace the cloudy sky with a clear sunset.”
    • “Add soft studio lighting and a modern background.”
    • “Turn the model’s outfit into a formal business suit.”

💰 Pricing

  • $0.019 per image

  • Commercial use allowed

💡 Best Use Cases

  • Marketing & Branding — Update campaign visuals without reshooting.
  • Product Photography — Adjust materials, lighting, or layout instantly.
  • Social Media & Content Creation — Generate multiple variations with minimal effort.
  • Artistic Design — Experiment with colors, styles, and compositions effortlessly.

📝 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.

300以上のモデルから始める、

すべてのモデルを探索

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