google/nano-banana-pro/text-to-image-developer

टेक्स्ट-से-इमेज

PRODEV

Open and Advanced Large-Scale Image Generative Models.

इनपुट

पैरामीटर कॉन्फ़िगरेशन लोड हो रहा है...

आउटपुट

निष्क्रिय

जेनरेट की गई छवियां यहां दिखाई देंगी

अपनी सेटिंग्स कॉन्फ़िगर करें और शुरू करने के लिए चलाएं पर क्लिक करें

आपके अनुरोध की लागत $0.084 प्रति रन होगी। $10 के साथ आप इस मॉडल को लगभग 119 बार चला सकते हैं।

आगे आप यह कर सकते हैं:

इमेज-से-वीडियो इमेज-से-इमेज

पैरामीटर

कोड उदाहरण
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/text-to-image-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()

इंस्टॉल करें

अपनी प्रोग्रामिंग भाषा के लिए आवश्यक पैकेज इंस्टॉल करें।

bash

pip install requests

प्रमाणीकरण

सभी API अनुरोधों के लिए API कुंजी के माध्यम से प्रमाणीकरण आवश्यक है। आप अपनी API कुंजी Atlas Cloud डैशबोर्ड से प्राप्त कर सकते हैं।

bash

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

HTTP हेडर

python

import os

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

अपनी API कुंजी सुरक्षित रखें

क्लाइंट-साइड कोड या सार्वजनिक रिपॉज़िटरी में अपनी API कुंजी कभी उजागर न करें। इसके बजाय एनवायरनमेंट वेरिएबल या बैकएंड प्रॉक्सी का उपयोग करें।

अनुरोध सबमिट करें

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())

अनुरोध सबमिट करें

एक असिंक्रोनस जनरेशन अनुरोध सबमिट करें। API एक प्रेडिक्शन ID लौटाता है जिसका उपयोग आप स्थिति जाँचने और परिणाम प्राप्त करने के लिए कर सकते हैं।

POST/api/v1/model/generateImage

अनुरोध बॉडी

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/text-to-image-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']}")

प्रतिक्रिया

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

स्थिति जाँचें

अपने अनुरोध की वर्तमान स्थिति जाँचने के लिए प्रेडिक्शन एंडपॉइंट को पोल करें।

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

पोलिंग उदाहरण

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)

स्थिति मान

processingअनुरोध अभी भी प्रोसेस हो रहा है।

completedजनरेशन पूर्ण हो गया है। आउटपुट उपलब्ध हैं।

succeededजनरेशन सफल रहा। आउटपुट उपलब्ध हैं।

failedजनरेशन विफल हो गया। एरर फ़ील्ड जाँचें।

पूर्ण प्रतिक्रिया

{
  "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"
  }
}

फ़ाइलें अपलोड करें

Atlas Cloud स्टोरेज पर फ़ाइलें अपलोड करें और एक URL प्राप्त करें जिसका उपयोग आप अपने API अनुरोधों में कर सकते हैं। अपलोड के लिए multipart/form-data का उपयोग करें।

POST/api/v1/model/uploadMedia

अपलोड उदाहरण

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}")

प्रतिक्रिया

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

Input Schema

अनुरोध बॉडी में निम्नलिखित पैरामीटर स्वीकार किए जाते हैं।

कुल: 0आवश्यक: 0वैकल्पिक: 0

कोई पैरामीटर उपलब्ध नहीं है।

अनुरोध बॉडी का उदाहरण

json

{
  "model": "google/nano-banana-pro/text-to-image-developer"
}

Output Schema

API जनरेट किए गए आउटपुट URL के साथ एक प्रेडिक्शन प्रतिक्रिया लौटाता है।

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

प्रतिक्रिया का उदाहरण

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 300+ AI मॉडल को सीधे आपके AI कोडिंग असिस्टेंट में इंटीग्रेट करता है। इंस्टॉल करने के लिए एक कमांड, फिर इमेज, वीडियो जनरेट करने और LLM के साथ चैट करने के लिए प्राकृतिक भाषा का उपयोग करें।

समर्थित क्लाइंट

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ समर्थित क्लाइंट

इंस्टॉल करें

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

API कुंजी सेटअप करें

Atlas Cloud डैशबोर्ड से अपनी API कुंजी प्राप्त करें और इसे एनवायरनमेंट वेरिएबल के रूप में सेट करें।

bash

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

क्षमताएँ

एक बार इंस्टॉल होने के बाद, आप सभी Atlas Cloud मॉडल तक पहुँचने के लिए अपने AI असिस्टेंट में प्राकृतिक भाषा का उपयोग कर सकते हैं।

इमेज जनरेशनNano Banana 2, Z-Image और अन्य मॉडल के साथ इमेज जनरेट करें।

वीडियो निर्माणKling, Vidu, Veo आदि के साथ टेक्स्ट या इमेज से वीडियो बनाएँ।

LLM चैटQwen, DeepSeek और अन्य बड़े भाषा मॉडल के साथ चैट करें।

मीडिया अपलोडइमेज एडिटिंग और इमेज-टू-वीडियो वर्कफ़्लो के लिए लोकल फ़ाइलें अपलोड करें।

और जानें

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server आपके IDE को Model Context Protocol के माध्यम से 300+ AI मॉडल से जोड़ता है। किसी भी MCP-संगत क्लाइंट के साथ काम करता है।

समर्थित क्लाइंट

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ समर्थित क्लाइंट

इंस्टॉल करें

bash

npx -y atlascloud-mcp

कॉन्फ़िगरेशन

अपने IDE की MCP सेटिंग्स फ़ाइल में निम्नलिखित कॉन्फ़िगरेशन जोड़ें।

json

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

उपलब्ध टूल

atlas_generate_imageटेक्स्ट प्रॉम्प्ट से इमेज जनरेट करें।

atlas_generate_videoटेक्स्ट या इमेज से वीडियो बनाएँ।

atlas_chatबड़े भाषा मॉडल के साथ चैट करें।

atlas_list_models300+ उपलब्ध AI मॉडल ब्राउज़ करें।

atlas_quick_generateऑटो मॉडल चयन के साथ एक-चरण कंटेंट निर्माण।

atlas_upload_mediaAPI वर्कफ़्लो के लिए लोकल फ़ाइलें अपलोड करें।

और जानें

github.com/AtlasCloudAI/mcp-server

API स्कीमा

स्कीमा उपलब्ध नहीं

अनुरोध इतिहास देखने के लिए कृपया साइन इन करें

अपने मॉडल अनुरोध इतिहास तक पहुंचने के लिए आपको साइन इन करना होगा।

साइन इन करें

Seedance 1.5 Pro

नेटिव ऑडियो-विजुअल जेनरेशन

साउंड और विज़न, सब कुछ एक ही टेक में

ByteDance का क्रांतिकारी AI मॉडल जो एक ही एकीकृत प्रक्रिया से पूरी तरह से सिंक्रनाइज़ ऑडियो और वीडियो को एक साथ जेनरेट करता है। 8+ भाषाओं में मिलीसेकंड-प्रिसिजन लिप-सिंक के साथ सच्चे नेटिव ऑडियो-विजुअल जेनरेशन का अनुभव करें।

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.

परफेक्ट फॉर

📸

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

तकनीकी विनिर्देश

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

नेटिव ऑडियो-विजुअल जेनरेशन का अनुभव करें

दुनिया भर के फिल्म निर्माताओं, विज्ञापनदाताओं और क्रिएटर्स के साथ शामिल हों जो Seedance 1.5 Pro की अभूतपूर्व तकनीक के साथ वीडियो कंटेंट क्रिएशन में क्रांति ला रहे हैं।

✨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