google/nano-banana-pro/edit-developer

इमेज-से-इमेज

PRODEV

Nano Banana Pro Edit Developer API by Google

google/nano-banana-pro/edit-developer

Edit-developer

Open and Advanced Large-Scale Image Generative Models.

इनपुट

प्रॉम्प्ट *

छवियाँ *(0/10)

आप फ़ाइल को ड्रैग और ड्रॉप कर सकते हैं या अपलोड के लिए क्लिक कर सकते हैं

MAX:10

पक्षानुपात

रिज़ॉल्यूशन

Enable web search

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 $0.07 प्रति रन होगी। $10 के साथ आप इस मॉडल को लगभग 142 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/edit-developer",  # Required. model name
    "aspect_ratio": "example_value",  # The aspect ratio of the generated media
    "enable_base64_output": False,  # If enabled, the output will be encoded into a BASE64 string instead of a URL
    "enable_sync_mode": False,  # If set to true, the function will wait for the result to be generated and uploaded before returning the response
    "enable_web_search": False,  # If enabled, the model will use web search to ground the generation with real-time information
    "images": [
        "https://example.com/image1.jpg"
    ],  # Required. List of URLs of input images for editing
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The positive prompt for the generation
    "resolution": "1k",  # The resolution of the output image. options: 1k | 2k | 4k
}

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।

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।

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

HTTP हेडर

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/edit-developer",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

प्रतिक्रिया

{
  "code": 200,
  "data": {
    "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: 8आवश्यक: 3वैकल्पिक: 5

modelstringrequired

model name

Default: "google/nano-banana-pro/edit-developer"

aspect_ratiostring

The aspect ratio of the generated media.

1:13:22:33:44:34:55:49:1616:921:9

enable_base64_outputboolean

If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Default: false

enable_sync_modeboolean

If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.

Default: false

enable_web_searchboolean

If enabled, the model will use web search to ground the generation with real-time information.

Default: false

imagesarray[string]required

List of URLs of input images for editing. The maximum number of images is 10.

Min items: 1Max items: 10

promptstringrequired

The positive prompt for the generation.

resolutionstring

The resolution of the output image.

Default: "1k"

1k2k4k

अनुरोध बॉडी का उदाहरण

{
  "model": "google/nano-banana-pro/edit-developer",
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "enable_web_search": false,
  "images": [
    "https://example.com/file.jpg"
  ],
  "prompt": "A beautiful landscape",
  "resolution": "1k"
}

Output Schema

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

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

statusstring

Status of the task: created, processing, completed, or failed.

प्रतिक्रिया का उदाहरण

{
  "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 400+ 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

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।

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 400+ 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

npx -y atlascloud-mcp

कॉन्फ़िगरेशन

अपने IDE की MCP सेटिंग्स फ़ाइल में निम्नलिखित कॉन्फ़िगरेशन जोड़ें।

{
  "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_models400+ 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

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "google/nano-banana-pro/edit-developer"
          },
          "aspect_ratio": {
            "description": "The aspect ratio of the generated media.",
            "enum": [
              "1:1",
              "3:2",
              "2:3",
              "3:4",
              "4:3",
              "4:5",
              "5:4",
              "9:16",
              "16:9",
              "21:9"
            ],
            "type": "string",
            "x-placeholder": "Select aspect ratio"
          },
          "enable_base64_output": {
            "default": false,
            "description": "If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.",
            "disabled": true,
            "type": "boolean"
          },
          "enable_sync_mode": {
            "default": false,
            "description": "If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.",
            "disabled": true,
            "type": "boolean"
          },
          "enable_web_search": {
            "default": false,
            "description": "If enabled, the model will use web search to ground the generation with real-time information.",
            "type": "boolean"
          },
          "images": {
            "description": "List of URLs of input images for editing. The maximum number of images is 10.",
            "items": {
              "type": "string"
            },
            "maxItems": 10,
            "minItems": 1,
            "type": "array",
            "x-ui-component": "uploaders"
          },
          "prompt": {
            "description": "The positive prompt for the generation.",
            "type": "string"
          },
          "resolution": {
            "default": "1k",
            "description": "The resolution of the output image.",
            "enum": [
              "1k",
              "2k",
              "4k"
            ],
            "type": "string"
          }
        },
        "required": [
          "model",
          "prompt",
          "images"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "images",
          "aspect_ratio",
          "resolution",
          "enable_web_search",
          "enable_sync_mode",
          "enable_base64_output"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction, the ID of the prediction to get.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated content (empty when status is not completed).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM के लिए प्रॉम्प्ट टेम्पलेट

# google/nano-banana-pro/edit-developer

> Open and Advanced Large-Scale Image Generative Models.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `google/nano-banana-pro/edit-developer`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"google/nano-banana-pro/edit-developer"`

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`images`** (`array[string]`, _required_):
  List of URLs of input images for editing. The maximum number of images is 10.
  - Min items: 1
  - Max items: 10

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the generated media.
  - Options: "1:1", "3:2", "2:3", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"

- **`resolution`** (`string`, _optional_):
  The resolution of the output image.
  - Default: `"1k"`
  - Options: "1k", "2k", "4k"

- **`enable_web_search`** (`boolean`, _optional_):
  If enabled, the model will use web search to ground the generation with real-time information.
  - Default: `false`

- **`enable_sync_mode`** (`boolean`, _optional_):
  If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
  - Default: `false`

- **`enable_base64_output`** (`boolean`, _optional_):
  If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "google/nano-banana-pro/edit-developer",
  "prompt": "",
  "images": [
    ""
  ]
}
```


**Full Example**:

```json
{
  "model": "google/nano-banana-pro/edit-developer",
  "prompt": "",
  "images": [
    ""
  ],
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "enable_web_search": false,
  "enable_sync_mode": false,
  "enable_base64_output": false
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/nano-banana-pro/edit-developer",
  "prompt": "",
  "images": [
    ""
  ],
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "enable_web_search": false,
  "enable_sync_mode": false,
  "enable_base64_output": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/google/nano-banana-pro/edit-developer)

Live action movie still, Japanese youth film style. A beautiful young woman in a collegiate outfit running through a campus gate, laughing. Dynamic motion, hair flying. Surrounded by falling pink cherry blossom petals. Soft sunlight, airy atmosphere, shallow depth of field, bokeh background of other students, Fujifilm color tone, realistic, sharp focus on eyes.

Translate the text in the image into Portuguese from brazil.

Make the hamburger made of glass.

Place the sofa from Figure 2 into Figure 1, and blend the light and shadows. The model merges these items into one cohesive interior scene, matching perspective, light direction, and color temperature automatically. For visual designers, this means realistic "in-context" images for furniture, décor, and home living categories — without 3D staging or studio photography.

Turn the image into Minecraft style.

Turn ithis scene into nighttime.

लोड हो रहा है...

Nano Banana - Google का क्रांतिकारी विज़ुअल AI

नया लॉन्च

जिसे Gemini 2.5 Flash Image भी कहा जाता है

मल्टीमॉडल AI तकनीक के क्षेत्र में Google की नवीनतम सफलता, जो बिजली जैसी तेज़ गति और बेहतरीन गुणवत्ता के साथ अभूतपूर्व इमेज जेनरेशन और एडिटिंग क्षमताएं प्रदान करती है।

उन्नत इमेज जेनरेशन

मल्टी-इमेज फ्यूज़न तकनीक
कई जेनरेशन में किरदार की निरंतरता
स्टाइल बनाए रखने वाला रूपांतरण
4K तक उच्च-रिज़ॉल्यूशन आउटपुट

स्मार्ट एडिटिंग टूल्स

टेक्स्ट-आधारित स्मार्ट एडिटिंग
ऑब्जेक्ट जोड़ना और हटाना
बैकग्राउंड रिप्लेसमेंट
स्टाइल ट्रांसफर और कलात्मक प्रभाव

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.

तकनीकी विशेषताएं

प्रदर्शन

बिजली जैसी तेज़ जेनरेशन

गति के लिए अनुकूलित, अधिकांश कार्यों के लिए जेनरेशन में 2 सेकंड से भी कम समय लगता है, जो इसे रियल-टाइम एप्लिकेशन और तेज़ प्रोटोटाइपिंग वर्कफ़्लो के लिए आदर्श बनाता है।

गुणवत्ता

बेहतरीन आउटपुट गुणवत्ता

Google की उन्नत AI आर्किटेक्चर का उपयोग करते हुए, सटीक प्रकाश, बनावट और संरचना के साथ अत्यधिक विस्तृत, फोटोरियलिस्टिक इमेज तैयार करता है।

नवाचार

नोवेल व्यू सिंथेसिस

क्रांतिकारी 2D से 3D रूपांतरण क्षमता, जो एक ही इमेज से कई दृष्टिकोण बनाने में सक्षम है और कंटेंट क्रिएशन के लिए नई संभावनाएं खोलती है।

उपयोग के मामले

📸

प्रोडक्ट फोटोग्राफी

🎨

डिजिटल आर्ट क्रिएशन

✨

फोटो एन्हांसमेंट

📊

मार्केटिंग विज़ुअल्स

👤

किरदार डिज़ाइन

👔

वर्चुअल ट्राई-ऑन

📱

सोशल मीडिया

🔄

फोटो रेस्टोरेशन

Nano Banana क्यों चुनें?

🚀

किसी सेटअप की ज़रूरत नहीं

किसी जटिल कॉन्फ़िगरेशन या इंस्टॉलेशन के बिना तुरंत क्रिएशन शुरू करें

🎯

सटीक नियंत्रण

सहज टेक्स्ट कमांड के ज़रिए अपनी रचना के हर पहलू को बारीकी से समायोजित करें

🔄

सुसंगत परिणाम

कई जेनरेशन में किरदार और स्टाइल की निरंतरता बनाए रखें

तकनीकी विनिर्देश

मॉडल आर्किटेक्चर:Google AI Studio द्वारा संचालित

प्रोसेसिंग स्पीड:औसत जेनरेशन समय < 2 सेकंड

रिज़ॉल्यूशन सपोर्ट:4096x4096 पिक्सेल तक

फॉर्मेट सपोर्ट:PNG, JPEG, WebP आउटपुट फॉर्मेट

मल्टीमॉडल इनपुट:टेक्स्ट, इमेज और संयुक्त प्रॉम्प्ट

API इंटीग्रेशन:पूर्ण दस्तावेज़ीकरण के साथ RESTful API

Nano Banana AI की शक्ति का अनुभव करें

हज़ारों क्रिएटर्स और व्यवसायों के साथ जुड़ें जो Google की सबसे उन्नत इमेज AI तकनीक से अपनी विज़ुअल कंटेंट को बदल रहे हैं।

✨शुरुआत के लिए निःशुल्क क्रेडिट

⚡तत्काल एक्सेस

🌐हर जगह काम करता है

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

समान मॉडल देखें

NEW

इमेज-से-इमेज

DEV

Nano Banana 2 Lite Edit Developer

Google's fastest and most cost-efficient Nano Banana image model for editing, applying natural-language edits and multi-image composition to up to 14 reference images with low latency.

Nano Banana 2 Lite Text-to-Image Developer

Google's fastest and most cost-efficient Nano Banana image model, turning natural-language text prompts into high-quality 1k images in as little as 4 seconds for rapid, high-volume generation.

Nano Banana 2 Lite Edit

Nano banana lite is the efficiency-focused model in the image generation family. Sub-2 second latency with cost-effective generation and editing, fast multi-turn local edits, and 14 supported aspect ratios.

Nano Banana 2 Lite Text-to-image

Nano Banana 2 Reference to Image

Google's advanced AI-powered video-to-image generation model, designed to generate high-quality static images from video clips combined with text instructions.

Nano Banana 2 Reference to Image Developer

Google's advanced AI-powered video-to-image generation model, designed to generate high-quality static images from video clips combined with text instructions.

Nano Banana 2 Text-to-Image Developer

Google's lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts.

Nano Banana 2 Text-to-Image

Google's lightweight yet powerful AI image generation model, built for creators who need fast, high-quality visuals from simple text prompts.

Nano Banana 2 Edit Developer

Google's advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words.

Nano Banana 2 Edit

Google's advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words.

Nano Banana Pro Text-to-image Ultra

Nano Banana Pro is the next-generation Nano Banana image model, delivering sharper detail, richer color control, and faster diffusion for production-ready visuals.

Nano Banana Pro Edit Ultra

Nano Banana Pro Edit is an image editing tool built on the Nano Banana model family, designed for precise, AI-powered visual adjustments.

Nano Banana Pro Text-to-image

Nano Banana Pro is the next-generation Nano Banana image model, delivering sharper detail, richer color control, and faster diffusion for production-ready visuals.

From

$0.14/छवि