Nano Banana Pro Edit Ultra API by Google
Nano Banana Pro Edit is an image editing tool built on the Nano Banana model family, designed for precise, AI-powered visual adjustments.
Đầu vào
Đầu ra
Nhàn rỗiMỗi lần chạy có giá $0.15. Với $10, bạn có thể chạy khoảng 66 lần.
Bạn có thể tiếp tục với:
Ví dụ mã
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-pro/edit-ultra",
"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()Cài đặt
Cài đặt gói cần thiết cho ngôn ngữ lập trình của bạn.
pip install requestsXác thực
Tất cả các yêu cầu API đều cần xác thực thông qua khóa API. Bạn có thể lấy khóa API từ bảng điều khiển Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Headers
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Không bao giờ để lộ khóa API trong mã phía máy khách hoặc kho lưu trữ công khai. Thay vào đó, hãy sử dụng biến môi trường hoặc proxy phía máy chủ.
Gửi yêu cầu
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())Gửi yêu cầu
Gửi yêu cầu tạo nội dung bất đồng bộ. API trả về một ID dự đoán mà bạn có thể sử dụng để kiểm tra trạng thái và lấy kết quả.
/api/v1/model/generateImageNội dung yêu cầu
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-pro/edit-ultra",
"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']}")Phản hồi
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Kiểm tra trạng thái
Truy vấn endpoint dự đoán để kiểm tra trạng thái hiện tại của yêu cầu.
/api/v1/model/prediction/{prediction_id}Ví dụ truy vấn
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)Giá trị trạng thái
processingYêu cầu vẫn đang được xử lý.completedQuá trình tạo đã hoàn tất. Kết quả đầu ra đã sẵn sàng.succeededQuá trình tạo thành công. Kết quả đầu ra đã sẵn sàng.failedQuá trình tạo thất bại. Kiểm tra trường lỗi.Phản hồi hoàn tất
{
"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"
}
}Tải tệp lên
Tải tệp lên bộ nhớ Atlas Cloud và nhận URL mà bạn có thể sử dụng trong các yêu cầu API của mình. Sử dụng multipart/form-data để tải lên.
/api/v1/model/uploadMediaVí dụ tải lên
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}")Phản hồi
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
Các tham số sau được chấp nhận trong nội dung yêu cầu.
Không có tham số nào.
Ví dụ nội dung yêu cầu
{
"model": "google/nano-banana-pro/edit-ultra"
}Output Schema
API trả về phản hồi dự đoán với các URL đầu ra đã tạo.
Ví dụ phản hồi
{
"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 tích hợp hơn 300 mô hình AI trực tiếp vào trợ lý lập trình AI của bạn. Một lệnh để cài đặt, sau đó sử dụng ngôn ngữ tự nhiên để tạo hình ảnh, video và trò chuyện với LLM.
Ứng dụng được hỗ trợ
Cài đặt
npx skills add AtlasCloudAI/atlas-cloud-skillsThiết lập khóa API
Lấy khóa API từ bảng điều khiển Atlas Cloud và đặt nó làm biến môi trường.
export ATLASCLOUD_API_KEY="your-api-key-here"Khả năng
Sau khi cài đặt, bạn có thể sử dụng ngôn ngữ tự nhiên trong trợ lý AI để truy cập tất cả các mô hình Atlas Cloud.
MCP Server
Atlas Cloud MCP Server kết nối IDE của bạn với hơn 300 mô hình AI thông qua Model Context Protocol. Hoạt động với bất kỳ ứng dụng tương thích MCP nào.
Ứng dụng được hỗ trợ
Cài đặt
npx -y atlascloud-mcpCấu hình
Thêm cấu hình sau vào tệp cài đặt MCP của IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Công cụ khả dụng
Schema API
Schema không khả dụngĐăng nhập để xem lịch sử yêu cầu
Bạn cần đăng nhập để truy cập lịch sử yêu cầu mô hình của mình.
Đăng nhậpSeedance 1.5 Pro
TẠO NỘI DUNG NGHE NHÌN GỐCÂm Thanh và Hình Ảnh, Tất Cả Trong Một Lần Quay
Mô hình AI đột phá của ByteDance tạo ra âm thanh và video đồng bộ hoàn hảo cùng lúc từ một quy trình thống nhất duy nhất. Trải nghiệm tạo nội dung nghe nhìn gốc thực sự với đồng bộ môi chính xác đến mili giây trên hơn 8 ngôn ngữ.
- Multi-image fusion technology
- Character consistency across generations
- Style-preserving transformations
- High-resolution output up to 4K
- 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 any photo into a realistic character figure with packaging and displayturn 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
Transform anime illustrations into realistic cosplay photographyGenerate 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
Transform people from photos into collectible action figures with custom packagingTransform 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
Transform photos into Funko Pop style collectible figures with custom packagingTransform 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
Transform product design sketches into photorealistic rendersturn this illustration of a perfume into a realistic version, Frosted glass bottle with a marble cap
Transform to Q-Version Character
Create cartoon characters with face shape reference controlTransform the person from image 1 into a Q-version character design based on the face shape from image 2
Building to 3D Architecture Model
Convert architectural photos into detailed physical modelsconvert 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
Optimized for speed with generation times under 2 seconds for most tasks, making it perfect for real-time applications and rapid prototyping workflows.
Leveraging Google's advanced AI architecture to produce highly detailed, photorealistic images with accurate lighting, textures, and compositions.
Revolutionary 2D-to-3D conversion capabilities enabling creation of multiple viewpoints from a single image, opening new possibilities for content creation.
Hoàn Hảo Cho
Why Choose Nano Banana?
No Setup Required
Start creating immediately without complex configurations or installationsPrecision Control
Fine-tune every aspect of your creation with intuitive text commandsConsistent Results
Maintain character and style consistency across multiple generationsThông Số Kỹ Thuật
Trải Nghiệm Tạo Nội Dung Nghe Nhìn Gốc
Tham gia cùng các nhà làm phim, nhà quảng cáo và người sáng tạo trên toàn thế giới đang cách mạng hóa việc tạo nội dung video với công nghệ đột phá của Seedance 1.5 Pro.
Nano Banana Pro : A state-of-the-art, multimodal reasoning and image generation model by Google DeepMind
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Nano Banana Pro (also known as Gemini 3 Pro Image) |
| Developer | Google DeepMind |
| Release Date | November 20, 2025 |
| Model Type | Multimodal Reasoning and Image Generation |
| Related Links | Official Product Page, Model Card (PDF) |
Introduction
Nano Banana Pro, officially designated as Gemini 3 Pro Image, represents the next generation in Google's series of highly-capable, natively multimodal models. It is designed for professional asset production, integrating the advanced reasoning capabilities of the Gemini 3 Pro foundation model with a sophisticated image generation engine. The primary goal of Nano Banana Pro is to provide users with studio-quality precision and control, enabling the creation of complex, high-fidelity visuals from textual and image-based prompts. Its core contribution lies in its ability to understand and execute intricate instructions, maintain character and scene consistency, and render legible text directly within generated images, setting a new standard for professional creative workflows.
Key Features & Innovations
Nano Banana Pro introduces several technical breakthroughs that distinguish it from prior models:
- Superior Text Rendering: The model excels at generating images that contain clear, accurate, and stylistically coherent text, making it ideal for creating posters, diagrams, and marketing materials.
- Advanced Creative Controls: Users can exercise fine-grained control over image outputs, including camera angles, lighting transformations (e.g., day to night), color grading, depth of field, and localized editing.
- High-Fidelity Consistency: It can maintain the consistency of up to 14 input images and blend up to 5 distinct characters seamlessly into complex compositions, ensuring visual coherence across a series of generated images.
- Deep Real-World Knowledge: Built on Gemini 3 Pro, the model leverages a vast understanding of the world to generate contextually rich and factually grounded visuals, from detailed infographics to historically accurate scenes.
- Multilingual Capabilities: The model can accurately render and translate text across multiple languages within an image, facilitating the localization of visual content.
- Complex Composition from Multiple Inputs: Nano Banana Pro can synthesize elements from multiple source images and text prompts to create a single, cohesive scene, enabling complex creative concepts.
Model Architecture & Technical Details
Nano Banana Pro's architecture is fundamentally based on the Gemini 3 Pro model. While specific architectural details are not fully disclosed, the following technical information is available:
- Foundation Model: Gemini 3 Pro
- Inputs: The model accepts text strings and images as input, with a large context window of up to 1 million tokens.
- Outputs: It generates high-resolution images (up to 4K) with a 64K token output capacity for handling complex generation tasks.
- Training Infrastructure:
- Hardware: The model was trained on Google's custom-designed Tensor Processing Units (TPUs), which are optimized for large-scale machine learning computations and high-bandwidth memory access.
- Software: The training process utilized JAX and ML Pathways, Google's high-performance frameworks for machine learning research.
- Knowledge Cutoff: The model's internal knowledge base has a cutoff date of January 2025.
Intended Use & Applications
Nano Banana Pro is intended for professional and creative applications that require a high degree of precision, control, and visual fidelity. It is well-suited for a variety of downstream tasks and application scenarios:
- Professional Content Creation: Generating production-ready assets for marketing campaigns, advertising, and branding.
- Design and Prototyping: Creating detailed product mockups, storyboards for film and animation, and architectural visualizations.
- Informational Graphics: Designing complex and accurate infographics, educational diagrams, and data visualizations.
- Artistic and Creative Expression: Enabling artists and designers to explore novel visual styles and create complex, multi-element compositions.
Performance
Nano Banana Pro's performance has been evaluated through extensive human evaluations and benchmarked against other leading image generation models. The results, measured in Elo scores, demonstrate its strong capabilities across a wide range of tasks.
A technical report also notes a performance dichotomy: while the model produces subjectively superior visual quality by hallucinating plausible details, it can lag behind specialist models in traditional quantitative metrics due to the stochastic nature of generative models.
Existing Capabilities (Elo Score Comparison)
| Capability | Gemini 3 Pro Image | Gemini 2.5 Flash Image | GPT-Image 1 | Seedream v4 4k | Flux Pro Kontext Max |
|---|---|---|---|---|---|
| Text Rendering | 1198 ± 18 | 997 ± 10 | 1150 ± 14 | 1019 ± 13 | 854 ± 13 |
| Stylization | 1098 ± 11 | 933 ± 7 | 1069 ± 9 | 991 ± 9 | 908 ± 11 |
| Multi-Turn | 1186 ± 19 | 1045 ± 24 | 1079 ± 32 | 990 ± 32 | 889 ± 37 |
| General Image Editing | 1127 ± 13 | 996 ± 8 | 1011 ± 13 | 965 ± 12 | 902 ± 13 |
| Character Editing | 1176 ± 16 | 1075 ± 8 | 1016 ± 10 | 889 ± 10 | 843 ± 10 |
| Object/Env. Editing | 1102 ± 19 | 1025 ± 9 | 930 ± 12 | 983 ± 13 | 961 ± 10 |
| General Text-to-Image | 1094 ± 16 | 1037 ± 8 | 1025 ± 9 | 1011 ± 9 | 907 ± 9 |
New Capabilities (Elo Score Comparison)
| Capability | Gemini 3 Pro Image | Gemini 2.5 Flash Image | GPT-Image 1 | Seedream v4 4k | Flux Pro Kontext Max |
|---|---|---|---|---|---|
| Multi-character Editing | 1213 ± 16 | 950 ± 10 | 997 ± 13 | 840 ± 19 | - |
| Chart Editing | 1209 ± 18 | 971 ± 10 | 994 ± 16 | 934 ± 16 | 893 ± 15 |
| Text Editing | 1202 ± 23 | 1001 ± 10 | 996 ± 14 | 860 ± 15 | 943 ± 12 |
| Factuality - Edu | 1169 ± 25 | 1050 ± 11 | 1084 ± 25 | 969 ± 22 | 884 ± 26 |
| Infographics | 1268 ± 17 | 1162 ± 11 | 1087 ± 12 | 1049 ± 12 | 824 ± 15 |
| Visual Design | 1104 ± 16 | 1083 ± 7 | 1028 ± 11 | 1038 ± 12 | 907 ± 11 |
