google/nano-banana/edit-developer

Open and Advanced Large-Scale Image Generative Models.

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Nano Banana Edit Developer
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Open and Advanced Large-Scale Image Generative Models.

입력

매개변수 구성 로드 중...

출력

대기
생성된 이미지가 여기에 표시됩니다
설정을 구성하고 실행을 클릭하여 시작하세요

요청당 $0.023가 소요됩니다. $10로 이 모델을 약 434번 실행할 수 있습니다.

다음으로 할 수 있는 작업:

파라미터

코드 예시

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()

설치

사용하는 언어에 필요한 패키지를 설치하세요.

bash
pip install requests

인증

모든 API 요청에는 API 키를 통한 인증이 필요합니다. Atlas Cloud 대시보드에서 API 키를 받을 수 있습니다.

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

HTTP 헤더

python
import os

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

클라이언트 측 코드나 공개 저장소에 API 키를 노출하지 마세요. 대신 환경 변수 또는 백엔드 프록시를 사용하세요.

요청 제출

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())

요청 제출

비동기 생성 요청을 제출합니다. API는 상태 확인 및 결과 조회에 사용할 수 있는 예측 ID를 반환합니다.

POST/api/v1/model/generateImage

요청 본문

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']}")

응답

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

상태 확인

예측 엔드포인트를 폴링하여 요청의 현재 상태를 확인합니다.

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

폴링 예시

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)

상태 값

processing요청이 아직 처리 중입니다.
completed생성이 완료되었습니다. 출력을 사용할 수 있습니다.
succeeded생성이 성공했습니다. 출력을 사용할 수 있습니다.
failed생성에 실패했습니다. 오류 필드를 확인하세요.

완료 응답

{
  "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"
  }
}

파일 업로드

Atlas Cloud 스토리지에 파일을 업로드하고 API 요청에 사용할 수 있는 URL을 받습니다. multipart/form-data를 사용하여 업로드합니다.

POST/api/v1/model/uploadMedia

업로드 예시

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}")

응답

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

입력 Schema

다음 매개변수가 요청 본문에서 사용 가능합니다.

전체: 0필수: 0선택: 0

사용 가능한 매개변수가 없습니다.

요청 본문 예시

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

출력 Schema

API는 생성된 출력 URL이 포함된 예측 응답을 반환합니다.

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

응답 예시

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 Skills는 300개 이상의 AI 모델을 AI 코딩 어시스턴트에 직접 통합합니다. 한 번의 명령으로 설치하고 자연어로 이미지, 동영상 생성 및 LLM과 대화할 수 있습니다.

지원 클라이언트

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 지원 클라이언트

설치

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

API 키 설정

Atlas Cloud 대시보드에서 API 키를 받아 환경 변수로 설정하세요.

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

기능

설치 후 AI 어시스턴트에서 자연어를 사용하여 모든 Atlas Cloud 모델에 접근할 수 있습니다.

이미지 생성Nano Banana 2, Z-Image 등의 모델로 이미지를 생성합니다.
동영상 제작Kling, Vidu, Veo 등으로 텍스트나 이미지에서 동영상을 만듭니다.
LLM 채팅Qwen, DeepSeek 등 대규모 언어 모델과 대화합니다.
미디어 업로드이미지 편집 및 이미지-동영상 변환 워크플로우를 위해 로컬 파일을 업로드합니다.

MCP Server

Atlas Cloud MCP Server는 Model Context Protocol을 통해 IDE와 300개 이상의 AI 모델을 연결합니다. MCP 호환 클라이언트에서 사용할 수 있습니다.

지원 클라이언트

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 지원 클라이언트

설치

bash
npx -y atlascloud-mcp

설정

다음 설정을 IDE의 MCP 설정 파일에 추가하세요.

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

사용 가능한 도구

atlas_generate_image텍스트 프롬프트로 이미지를 생성합니다.
atlas_generate_video텍스트나 이미지로 동영상을 만듭니다.
atlas_chat대규모 언어 모델과 대화합니다.
atlas_list_models300개 이상의 사용 가능한 AI 모델을 탐색합니다.
atlas_quick_generate자동 모델 선택으로 원스텝 콘텐츠 생성.
atlas_upload_mediaAPI 워크플로우를 위해 로컬 파일을 업로드합니다.

API 스키마

스키마를 사용할 수 없음

요청 기록을 보려면 로그인하세요

모델 요청 기록에 액세스하려면 로그인해야 합니다.

로그인

Seedance 1.5 Pro

네이티브 오디오-비주얼 동기화 생성

사운드와 비전, 원테이크로 완벽 동기화

ByteDance의 혁신적인 AI 모델로 단일 통합 프로세스에서 완벽하게 동기화된 오디오와 비디오를 동시에 생성합니다. 8개 이상의 언어에서 밀리초 단위 정밀도의 립싱크를 제공하는 진정한 네이티브 오디오-비주얼 생성을 경험하세요.

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.

완벽한 활용

📸
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

기술 사양

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

네이티브 오디오-비주얼 생성 경험

Seedance 1.5 Pro의 획기적인 기술로 비디오 콘텐츠 제작을 혁신하고 있는 전 세계 영화 제작자, 광고주, 크리에이터들과 함께하세요.

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.

300개 이상의 모델로 시작하세요,

모든 모델 탐색

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