google/nano-banana-pro/edit-developer

image-vers-image

PRODEV

Open and Advanced Large-Scale Image Generative Models.

Entrée

Chargement de la configuration des paramètres...

Sortie

Inactif

Les images générées apparaîtront ici

Configurez vos paramètres et cliquez sur exécuter pour commencer

Votre requête coûtera $0.084 par exécution. Avec $10, vous pouvez exécuter ce modèle environ 119 fois.

Vous pouvez continuer avec :

Image-vers-Vidéo Image-vers-Image

Paramètres

Exemple de code
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-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()

Installer

Installez le package requis pour votre langage.

bash

pip install requests

Authentification

Toutes les requêtes API nécessitent une authentification via une clé API. Vous pouvez obtenir votre clé API depuis le tableau de bord Atlas Cloud.

bash

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

En-têtes HTTP

python

import os

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

Protégez votre clé API

N'exposez jamais votre clé API dans du code côté client ou dans des dépôts publics. Utilisez plutôt des variables d'environnement ou un proxy backend.

Soumettre une requête

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())

Soumettre une requête

Soumettez une requête de génération asynchrone. L'API renvoie un identifiant de prédiction que vous pouvez utiliser pour vérifier le statut et récupérer le résultat.

POST/api/v1/model/generateImage

Corps de la requête

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-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']}")

Réponse

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

Vérifier le statut

Interrogez le point de terminaison de prédiction pour vérifier le statut actuel de votre requête.

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

Exemple d'interrogation

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)

Valeurs de statut

processingLa requête est encore en cours de traitement.

completedLa génération est terminée. Les résultats sont disponibles.

succeededLa génération a réussi. Les résultats sont disponibles.

failedLa génération a échoué. Vérifiez le champ d'erreur.

Réponse terminée

{
  "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"
  }
}

Télécharger des fichiers

Téléchargez des fichiers vers le stockage Atlas Cloud et obtenez une URL utilisable dans vos requêtes API. Utilisez multipart/form-data pour le téléchargement.

POST/api/v1/model/uploadMedia

Exemple de téléchargement

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}")

Réponse

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

Schema d'entrée

Les paramètres suivants sont acceptés dans le corps de la requête.

Total: 0Requis: 0Optionnel: 0

Aucun paramètre disponible.

Exemple de corps de requête

json

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

Schema de sortie

L'API renvoie une réponse de prédiction avec les URL des résultats générés.

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

Exemple de réponse

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 intègre plus de 300 modèles d'IA directement dans votre assistant de codage IA. Une seule commande pour installer, puis utilisez le langage naturel pour générer des images, des vidéos et discuter avec des LLM.

Clients pris en charge

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clients pris en charge

Installer

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurer la clé API

Obtenez votre clé API depuis le tableau de bord Atlas Cloud et définissez-la comme variable d'environnement.

bash

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

Fonctionnalités

Une fois installé, vous pouvez utiliser le langage naturel dans votre assistant IA pour accéder à tous les modèles Atlas Cloud.

Génération d'imagesGénérez des images avec des modèles comme Nano Banana 2, Z-Image, et plus encore.

Création de vidéosCréez des vidéos à partir de texte ou d'images avec Kling, Vidu, Veo, etc.

Chat LLMDiscutez avec Qwen, DeepSeek et d'autres grands modèles de langage.

Téléchargement de médiasTéléchargez des fichiers locaux pour l'édition d'images et les workflows image-vers-vidéo.

En savoir plus

github.com/AtlasCloudAI/atlas-cloud-skills

Serveur MCP

Le serveur MCP Atlas Cloud connecte votre IDE avec plus de 300 modèles d'IA via le Model Context Protocol. Compatible avec tout client compatible MCP.

Clients pris en charge

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clients pris en charge

Installer

bash

npx -y atlascloud-mcp

Configuration

Ajoutez la configuration suivante au fichier de paramètres MCP de votre IDE.

json

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

Outils disponibles

atlas_generate_imageGénérez des images à partir de prompts textuels.

atlas_generate_videoCréez des vidéos à partir de texte ou d'images.

atlas_chatDiscutez avec de grands modèles de langage.

atlas_list_modelsParcourez plus de 300 modèles d'IA disponibles.

atlas_quick_generateCréation de contenu en une étape avec sélection automatique du modèle.

atlas_upload_mediaTéléchargez des fichiers locaux pour les workflows API.

En savoir plus

github.com/AtlasCloudAI/mcp-server

Schéma API

Schéma non disponible

Veuillez vous connecter pour voir l'historique des requêtes

Vous devez vous connecter pour accéder à l'historique de vos requêtes de modèle.

Se Connecter

Seedance 1.5 Pro

GÉNÉRATION AUDIO-VISUELLE NATIVE

Son et Image, Tout en Une Seule Prise

Le modèle d'IA révolutionnaire de ByteDance qui génère simultanément de l'audio et de la vidéo parfaitement synchronisés à partir d'un processus unifié unique. Découvrez la véritable génération audio-visuelle native avec une synchronisation labiale d'une précision milliseconde dans plus de 8 langues.

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.

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 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

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.

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.

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

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

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.

Examples curated from ZHO-ZHO-ZHO's Collection • 46+ creative use cases available

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.

Parfait Pour

📸

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

Spécifications Techniques

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

Découvrez la Génération Audio-Visuelle Native

Rejoignez les cinéastes, annonceurs et créateurs du monde entier qui révolutionnent la création de contenu vidéo avec la technologie révolutionnaire de Seedance 1.5 Pro.

✨Free Credits to Start

⚡Instant Access

🌐Works Everywhere

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