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.
INPUT
OUTPUT
IdleYour request will cost $0.15 per run. For $10 you can run this model approximately 66 times.
Here's what you can do next:
Esempio di codice
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()Installa
Installa il pacchetto richiesto per il tuo linguaggio.
pip install requestsAutenticazione
Tutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Header HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Non esporre mai la tua chiave API nel codice lato client o nei repository pubblici. Utilizza invece variabili d'ambiente o un proxy backend.
Invia una richiesta
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())Invia una richiesta
Invia una richiesta di generazione asincrona. L'API restituisce un ID di previsione che puoi usare per controllare lo stato e recuperare il risultato.
/api/v1/model/generateImageCorpo della richiesta
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']}")Risposta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Controlla lo stato
Interroga l'endpoint di previsione per verificare lo stato attuale della tua richiesta.
/api/v1/model/prediction/{prediction_id}Esempio di polling
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)Valori di stato
processingLa richiesta Γ¨ ancora in fase di elaborazione.completedLa generazione Γ¨ completata. I risultati sono disponibili.succeededLa generazione Γ¨ riuscita. I risultati sono disponibili.failedLa generazione Γ¨ fallita. Controlla il campo errore.Risposta completata
{
"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"
}
}Carica file
Carica file nello storage Atlas Cloud e ottieni un URL utilizzabile nelle tue richieste API. Usa multipart/form-data per il caricamento.
/api/v1/model/uploadMediaEsempio di caricamento
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}")Risposta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema di input
I seguenti parametri sono accettati nel corpo della richiesta.
Nessun parametro disponibile.
Esempio di corpo della richiesta
{
"model": "google/nano-banana-pro/edit-ultra"
}Schema di output
L'API restituisce una risposta di previsione con gli URL degli output generati.
Esempio di risposta
{
"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 integra oltre 300 modelli di IA direttamente nel tuo assistente di codifica IA. Un comando per installare, poi usa il linguaggio naturale per generare immagini, video e chattare con LLM.
Client supportati
Installa
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigura chiave API
Ottieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"FunzionalitΓ
Una volta installato, puoi usare il linguaggio naturale nel tuo assistente IA per accedere a tutti i modelli Atlas Cloud.
Server MCP
Il server MCP di Atlas Cloud collega il tuo IDE con oltre 300 modelli di IA tramite il Model Context Protocol. Funziona con qualsiasi client compatibile MCP.
Client supportati
Installa
npx -y atlascloud-mcpConfigurazione
Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Strumenti disponibili
API Schema
Schema not availablePlease log in to view request history
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Log InSeedance 1.5 Pro
GENERAZIONE AUDIO-VISIVA NATIVASuono e Visione, Tutto in Una Sola Ripresa
Il rivoluzionario modello di IA di ByteDance che genera audio e video perfettamente sincronizzati simultaneamente da un unico processo unificato. Sperimenta la vera generazione audio-visiva nativa con sincronizzazione labiale di precisione millimetrica in oltre 8 lingue.
- 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.
Perfetto Per
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 generationsSpecifiche Tecniche
Sperimenta la Generazione Audio-Visiva Nativa
Unisciti a cineasti, inserzionisti e creatori di tutto il mondo che stanno rivoluzionando la creazione di contenuti video con la tecnologia rivoluzionaria di 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 Β± 18 | 997 Β± 10 | 1150 Β± 14 | 1019 Β± 13 | 854 Β± 13 |
| Stylization | 1098 Β± 11 | 933 Β± 7 | 1069 Β± 9 | 991 Β± 9 | 908 Β± 11 |
| Multi-Turn | 1186 Β± 19 | 1045 Β± 24 | 1079 Β± 32 | 990 Β± 32 | 889 Β± 37 |
| General Image Editing | 1127 Β± 13 | 996 Β± 8 | 1011 Β± 13 | 965 Β± 12 | 902 Β± 13 |
| Character Editing | 1176 Β± 16 | 1075 Β± 8 | 1016 Β± 10 | 889 Β± 10 | 843 Β± 10 |
| Object/Env. Editing | 1102 Β± 19 | 1025 Β± 9 | 930 Β± 12 | 983 Β± 13 | 961 Β± 10 |
| General Text-to-Image | 1094 Β± 16 | 1037 Β± 8 | 1025 Β± 9 | 1011 Β± 9 | 907 Β± 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 Β± 16 | 950 Β± 10 | 997 Β± 13 | 840 Β± 19 | - |
| Chart Editing | 1209 Β± 18 | 971 Β± 10 | 994 Β± 16 | 934 Β± 16 | 893 Β± 15 |
| Text Editing | 1202 Β± 23 | 1001 Β± 10 | 996 Β± 14 | 860 Β± 15 | 943 Β± 12 |
| Factuality - Edu | 1169 Β± 25 | 1050 Β± 11 | 1084 Β± 25 | 969 Β± 22 | 884 Β± 26 |
| Infographics | 1268 Β± 17 | 1162 Β± 11 | 1087 Β± 12 | 1049 Β± 12 | 824 Β± 15 |
| Visual Design | 1104 Β± 16 | 1083 Β± 7 | 1028 Β± 11 | 1038 Β± 12 | 907 Β± 11 |
