模型
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聯絡
聯絡
模型
圖視頻模型
創造並擴展視覺想像力。
LLM
驅動智慧推理與對話。
Explore價格文檔Enterprise

google/nano-banana-2/edit

Google's advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words.

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Google Gemini, Imagen & Veo Model Families
Nano Banana2 Models
google/nano-banana-2/edit
Nano Banana 2 Edit
圖生圖

Google's advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words.

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 0.08。$10 可執行約 125 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/edit",
    "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-2/edit",
    "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-2/edit"
}

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。
了解更多

github.com/AtlasCloudAI/atlas-cloud-skills

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。
了解更多

github.com/AtlasCloudAI/mcp-server

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 2 — Pro 級品質，Flash 級速度

V2

又稱 Gemini 3.1 Flash Image

Google DeepMind 最新影像模型，結合了 Nano Banana Pro 的進階能力與 Gemini Flash 的極速效能 — 生成速度快 3-5 倍，支援最高 4K 解析度，單一工作流程中可維持多達 5 個角色的一致性。

新一代影像生成
  • 最高 4K 解析度輸出（512px / 1K / 2K / 4K 四階）
  • 10+ 種長寬比，包括 21:9、1:4、8:1 等
  • 精準清晰的圖中文字渲染
  • 接近 Pro 級畫質（約 95%），Flash 級速度
智慧編輯與一致性
  • 跨場景最多 5 個角色的一致性維持
  • 單一工作流程中最多 14 個物件的保真度
  • 透過自然語言進行精準編輯（移除、替換、調整姿態）
  • 多圖融合與無縫合成

Nano Banana 2 新功能

比 Pro 快 3-5 倍

採用 Gemini 3.1 Flash 架構，Nano Banana 2 標準影像生成僅需 4-8 秒，而 Nano Banana Pro 需要 10-20 秒。

影像搜尋增強

NB2 的亮點功能 — 生成過程中可透過 Google 搜尋擷取真實世界參考影像，大幅提升地標、名人和品牌標誌的生成準確度。

精準文字渲染

為行銷素材、賀卡和在地化內容生成準確清晰的文字，甚至可以在影像中直接翻譯和在地化文字。

多角色一致性

跨場景維持最多 5 個角色和 14 個物件的視覺一致性 — 非常適合分鏡、漫畫創作和行銷活動。

Prompt Examples & Templates

Explore curated prompt templates showcasing Nano Banana 2's key capabilities — text rendering, character consistency, search grounding, and 4K output.

Marketing Mockup with Text
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

Multi-Scene Character
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

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 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.

Real-World Reference Generation
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 Design Render
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

Artistic Style Transformation
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

Ultra High Resolution Scene
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

Nano Banana 2 is powered by Gemini 3.1 Flash Image — Google DeepMind

使用情境

🎬
分鏡與漫畫創作
📸
產品攝影
📊
行銷設計稿
📱
社群媒體內容
🔤
文字疊加設計
👤
角色設計
✨
照片編輯與修圖
🎨
品牌視覺內容

為什麼選擇 Nano Banana 2？

⚡

Flash 級速度

比 Nano Banana Pro 快 3-5 倍，標準生成時間僅 4-8 秒
🎯

接近 Pro 級畫質

在大多數情境下可達到 Pro 約 95% 的畫質水準
💰

更具性價比

成本約為 Nano Banana Pro 的一半 — 讓高品質 AI 影像生成更加普及

技術規格

架構：Gemini 3.1 Flash (GEMPIX2)
解析度支援：512px 至 4K（512px / 1K / 2K / 4K 四階）
長寬比：1:1, 4:3, 3:4, 2:3, 3:2, 16:9, 9:16, 1:4, 4:1, 8:1, 21:9
一致性：單一工作流程最多 5 個角色 + 14 個物件
內容安全：SynthID 浮水印，相容 C2PA 標準
API 存取：Gemini API、Vertex AI、AI Studio、Gemini CLI

立即體驗 Nano Banana 2

Pro 級畫質 Flash 級速度 — 輕鬆建立具有角色一致性、文字渲染和 4K 解析度支援的精美視覺內容。

✨免費額度立即開始
⚡即時 API 存取
🌐無需任何設定

Google Nano Banana 2 Edit

Nano Banana 2 Edit (Gemini 3.1 Flash Image) 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.

Why Choose This?

  • Natural language editing Modify images using simple text instructions — the model understands context and relationships.

  • Multi-image reference Upload up to 14 reference images for complex edits and compositions.

  • 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.

  • Prompt Enhancer Built-in tool to automatically improve your edit descriptions.

  • Format choice Export in PNG or JPEG format.

Parameters

ParameterRequiredDescription
imagesYesReference images to edit (max: 14, click "+ Add Item" to add more)
promptYesText description of the desired edit
aspect_ratioNoAspect ratio: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolutionNoOutput resolution: 1k (default), 2k, 4k
output_formatNoOutput format: png (default), jpeg

How to Use

  1. Upload reference images — add the images you want to edit (up to 14 images).
  2. Write your prompt — describe the edit clearly (e.g., "Change the man to a woman").
  3. Choose aspect ratio (optional) — select a preset or leave empty for default.
  4. Select resolution — choose 1K, 2K, or 4K based on your needs.
  5. Choose output format — PNG for transparency support, JPEG for smaller file size.
  6. Use Prompt Enhancer (optional) — click to automatically refine your description.
  7. Run — submit and download your edited image.

Pricing

ResolutionCost
1k$0.08
2k$0.12
4k$0.16

Best Use Cases

  • Character Modification — Change attributes like gender, age, clothing, or appearance.
  • Object Replacement — Swap elements within images while preserving context.
  • Style Transfer — Apply different visual styles to existing images.
  • Text Editing — Modify on-image text while maintaining design consistency.
  • Scene Adjustment — Change backgrounds, lighting, or environmental elements.

Pro Tips

  • Use clear, specific edit instructions for best results (e.g., "Change the man to a woman" rather than "modify the person").
  • Start with fewer reference images (1–3) for simpler edits.
  • More reference images can help with complex compositions but may affect stability.
  • 2K offers the best value — same price as 1K with higher resolution.
  • Try the Prompt Enhancer to automatically improve your descriptions.

Notes

  • Both images and prompt are required fields.
  • Maximum reference images: 14 (recommended: fewer images for better stability).
  • 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.

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