google/nano-banana/edit-developer

इमेज-से-इमेज

DEV

Nano Banana Edit Developer API by Google

google/nano-banana/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

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 $0.019 प्रति रन होगी। $10 के साथ आप इस मॉडल को लगभग 526 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/edit-developer",  # Required. model name
    "aspect_ratio": "example_value",  # The aspect ratio of the generated media
    "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 image generation
    "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
}

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/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: 6आवश्यक: 3वैकल्पिक: 3

modelstringrequired

model name

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

aspect_ratiostring

The aspect ratio of the generated media.

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

imagesarray[string]required

List of URLs of input images for editing.

Min items: 1Max items: 10

promptstringrequired

The positive prompt for image generation.

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

अनुरोध बॉडी का उदाहरण

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

Output Schema

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

idstring

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

modelstring

Model ID used for the prediction.

statusstring

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

outputsarray

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

created_atstring

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

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

LLM के लिए प्रॉम्प्ट टेम्पलेट

# google/nano-banana/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/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/edit-developer"`

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

- **`images`** (`array[string]`, _required_):
  List of URLs of input images for editing.
  - 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"

- **`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`

- **`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`



**Required Parameters Example**:

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


**Full Example**:

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


### Output Schema

The API returns the following output format:


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

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

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

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

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

- **`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.



**Example Response**:

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


## 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/edit-developer",
  "prompt": "",
  "images": [
    ""
  ],
  "aspect_ratio": "1:1",
  "enable_base64_output": false,
  "enable_sync_mode": 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/edit-developer)

Remove the text from the image of the person and put the glasses from the other image on the person.

Remove the text from the image and change the glowing color of the distant ball to blue.

Remove the text from the image and sharpen the blurry areas of the hands.

Adjust the figures in the image to real people.

Design a fully rendered character with a complete body, standing on the lunar surface. Ensure the character is well-proportioned, with attention to details like clothing, gear, and facial features. Set the scene with a realistic lunar backdrop, featuring craters, dusty terrain, and Earth visible in the distance. The lighting should reflect the stark, sharp shadows typical of the moon’s surface.

Remove the text from the image and adjust the character to a real person.

Based on the image, adjust the pose to show the person making a peace sign with both hands, expressing a happy expression.

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

Google Nano-Banana Edit

Nano-Banana Edit is Google’s advanced AI-powered image editing and generation model, designed to make visual transformation as intuitive as describing it in words. Built on Google’s cutting-edge computer vision and generative research, it combines precision, flexibility, and semantic awareness for professional-grade editing.

Difference to Nano Banana Edit: This model is cheaper and less stable than the version of Nano Banana Edit.

Try the New Version of Nano Banana!

🌟 Why it stands out

Natural Language Editing Modify images using simple text instructions — no masking, layering, or manual tools required.
Context-Aware Understanding Accurately interprets scene structure, spatial relationships, and object semantics for realistic results.
Style and Tone Preservation Keeps lighting, shadows, and texture consistent with the original image while applying changes seamlessly.
High Precision Control Excels at fine-grained edits such as color adjustments, object replacement, or composition shifts with minimal distortion.
Creative Versatility Suitable for concept art, photography, advertising design, and everyday content creation.

⚙️ How to use

Input: existing image + text prompt
Output: edited image (JPEG/PNG/WEBP)
Size: 1:1, 4:3, 16:9, 21:9, and so on.
Supports style transfer, relighting, background replacement, and object modification
Works with natural prompts like:
- “Replace the cloudy sky with a clear sunset.”
- “Add soft studio lighting and a modern background.”
- “Turn the model’s outfit into a formal business suit.”

💰 Pricing

$0.019 per image
Commercial use allowed

💡 Best Use Cases

Marketing & Branding — Update campaign visuals without reshooting.
Product Photography — Adjust materials, lighting, or layout instantly.
Social Media & Content Creation — Generate multiple variations with minimal effort.
Artistic Design — Experiment with colors, styles, and compositions effortlessly.

📝 Notes

Please ensure your prompts comply with Google’s Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.

समान मॉडल देखें

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इमेज-से-इमेज

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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