google/nano-banana/edit-developer

Open and Advanced Large-Scale Image Generative Models.

IMAGE-TO-IMAGENEW
Nano Banana Edit Developer
圖生圖
DEV

Open and Advanced Large-Scale Image Generative Models.

輸入

正在載入參數設定...

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 $0.023。$10 可執行約 434 次。

你可以繼續:

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/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()

安裝

為您的程式語言安裝所需的套件。

bash
pip install requests

驗證

所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。

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

HTTP 標頭

python
import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}
請妥善保管您的 API 金鑰

切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改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

提交非同步生成請求。API 會傳回一個預測 ID,您可以用它來檢查狀態並取得結果。

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/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']}")

回應

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

檢查狀態

輪詢預測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。輸出已可取得。
succeeded生成成功。輸出已可取得。
failed生成失敗。請檢查錯誤欄位。

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

上傳檔案至 Atlas Cloud 儲存空間並取得 URL,可用於您的 API 請求。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
  }
}

輸入 Schema

以下參數可在請求主體中使用。

總計: 0必填: 0選填: 0

無可用參數。

範例請求主體

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

輸出 Schema

API 傳回包含生成輸出 URL 的預測回應。

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

範例回應

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 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝,即可使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

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API 金鑰

從 Atlas Cloud 儀表板取得 API 金鑰,並設為環境變數。

bash
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,用於圖片編輯和圖片轉影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。

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

bash
npx -y atlascloud-mcp

設定

將以下設定新增至您 IDE 的 MCP 設定檔中。

json
{
  "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 300 多個可用的 AI 模型。
atlas_quick_generate一步完成內容創建,自動選擇模型。
atlas_upload_media上傳本機檔案用於 API 工作流程。

API Schema

Schema 不可用

請登入以檢視請求歷史

您需要登入才能存取模型請求歷史記錄。

登入

Nano Banana - Google 革命性視覺 AI

新功能

又稱 Gemini 2.5 Flash Image

Google 最新的多模態 AI 技術突破,提供前所未有的圖像生成和編輯功能,具有閃電般的速度和卓越的品質。

高級圖像生成
  • 多圖融合技術
  • 跨生成的角色一致性
  • 風格保留轉換
  • 高達 4K 的高分辨率輸出
智能編輯工具
  • 基於文本的智能編輯
  • 對象添加和移除
  • 背景替換
  • 風格轉移和藝術效果

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.

技術亮點

性能
閃電般的生成速度

優化速度,大多數任務的生成時間不到 2 秒,非常適合實時應用和快速原型開發工作流。

品質
卓越的輸出品質

利用 Google 先進的 AI 架構生成高度詳細、逼真的圖像,具有精確的照明、紋理和構圖。

創新
新穎的視圖合成

革命性的 2D 到 3D 轉換功能,可從單個圖像創建多個視點,為內容創作開闢新可能性。

使用案例

📸
產品攝影
🎨
數字藝術創作
照片增強
📊
營銷視覺效果
👤
角色設計
👔
虛擬試穿
📱
社交媒體
🔄
照片修復

為什麼選擇 Nano Banana?

🚀
無需設置
無需複雜配置或安裝即可立即開始創建
🎯
精密控制
通過直觀的文本命令微調創作的每個方面
🔄
一致的結果
在多代生成中保持角色和風格的一致性

技術規格

模型架構:Google AI Studio 驅動
處理速度:< 2 秒平均生成時間
分辨率支持:最高 4096x4096 像素
格式支持:PNG、JPEG、WebP 輸出格式
多模態輸入:文本、圖像和組合提示
API 集成:RESTful API,提供全面文檔

體驗 Nano Banana AI 的力量

加入數千名已經使用 Google 最先進的圖像 AI 技術改變視覺內容的創意人和企業。

免費積分可開始
即時訪問
🌐隨處可用

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+ 模型,即刻開啟,

探索全部模型

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