google/nano-banana-2/reference-to-image-developer

image-to-image

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Nano Banana 2 Reference-to-Image Developer API by Google

google/nano-banana-2/reference-to-image-developer

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.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.056 per run. For $10 you can run this model approximately 178 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
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-2/reference-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()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

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

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

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())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

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-2/reference-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']}")

Response

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

Check Status

Poll the prediction endpoint to check the current status of your request.

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

Polling Example

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)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "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"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

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}")

Response

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

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

{
  "model": "google/nano-banana-2/reference-to-image-developer"
}

Output Schema

The API returns a prediction response with the generated output URLs.

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

Example Response

{
  "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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

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

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 300+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

No examples available

Please log in to view request history

You need to be logged in to access your model request history.

Next-Gen Image Generation

Up to 4K resolution output (512px / 1K / 2K / 4K tiers)
10+ aspect ratios including 21:9, 1:4, 8:1 and more
Accurate and legible text rendering within images
Near Pro-level quality (~95%) at Flash speed

Intelligent Editing & Consistency

Character consistency for up to 5 characters across scenes
Object fidelity for up to 14 objects in one workflow
Targeted edits via natural language (remove, replace, repose)
Multi-image blending and seamless composition

What's New in Nano Banana 2

3-5x Faster Than Pro

Built on the Gemini 3.1 Flash architecture, Nano Banana 2 generates standard images in 4-8 seconds — compared to 10-20 seconds for Nano Banana Pro.

Image Search Grounding

The standout feature of NB2 — it can retrieve real-world reference images via Google Search during generation, significantly improving accuracy for landmarks, famous people, and brand logos.

Accurate Text Rendering

Generate accurate, legible text for marketing mockups, greeting cards, and localized content. You can even translate and localize text within an image.

Multi-Character Consistency

Maintain visual consistency for up to 5 characters and 14 objects across scenes — perfect for storyboards, comics, and marketing campaigns.

Text Rendering

Marketing Mockup with Text

Generate marketing visuals with accurate, legible text — one of NB2's standout improvements

Prompt

A minimalist coffee shop promotional poster with the text 'MORNING BREW — Fresh Roasted Daily' in elegant serif font, warm earth tones, steam rising from a ceramic cup, clean layout with plenty of whitespace

Character Consistency

Multi-Scene Character

Maintain character consistency across multiple scenes — supports up to 5 characters per workflow

Prompt

A young woman with short red hair and freckles, wearing a green jacket, standing in a rainy Tokyo street at night with neon reflections on wet pavement, cinematic lighting, photorealistic

Photo to Action Figure

Person to Action Figure

Transform people from photos into collectible action figures with custom packaging

Prompt

Transform 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.

Search Grounding

Real-World Reference Generation

Leverage Image Search Grounding to generate accurate real-world subjects like landmarks and brands

Prompt

A photorealistic aerial view of the Eiffel Tower at golden hour, with the Seine River winding through Paris below, warm sunset light casting long shadows, high detail, 4K resolution

Product Photography

Product Design Render

Create professional product photography with precise control over lighting and composition

Prompt

A frosted glass perfume bottle with a marble cap on a white marble surface, soft studio lighting from the left, subtle reflections, minimalist luxury aesthetic, product photography style

Style Transfer

Artistic Style Transformation

Apply diverse artistic styles while maintaining subject integrity

Prompt

Transform this photo into Studio Ghibli animation style, keeping the same composition and subjects, lush watercolor backgrounds, soft diffused lighting, whimsical atmosphere

4K Output

Ultra High Resolution Scene

Generate detailed scenes at up to 4K resolution with rich textures

Prompt

A cozy Japanese ramen shop interior at night, steam rising from bowls, warm amber lighting, detailed wooden counter with various condiments, a chef working in the background, 4K, ultra detailed

Use Cases

🎬

Storyboarding & Comics

📸

Product Photography

📊

Marketing Mockups

📱

Social Media Content

🔤

Text Overlay Design

👤

Character Design

✨

Photo Editing & Retouching

🎨

Brand Visual Content

Why Choose Nano Banana 2?

⚡

Flash Speed

3-5x faster than Nano Banana Pro with 4-8 seconds standard generation time

🎯

Near-Pro Quality

Achieves approximately 95% of Pro's image quality in most scenarios

💰

Cost Effective

Roughly half the cost of Nano Banana Pro — making high-quality AI image generation more accessible

Technical Specifications

Architecture:Gemini 3.1 Flash (GEMPIX2)

Resolution Support:512px to 4K (512px / 1K / 2K / 4K tiers)

Aspect Ratios:1:1, 4:3, 3:4, 2:3, 3:2, 16:9, 9:16, 1:4, 4:1, 8:1, 21:9

Consistency:Up to 5 characters + 14 objects per workflow

Content Safety:SynthID watermark, C2PA standard compatible

API Access:Gemini API, Vertex AI, AI Studio, Gemini CLI

Experience Nano Banana 2

Pro-level image generation at Flash speed — create stunning visuals with character consistency, text rendering, and 4K resolution support.

✨Free Credits to Start

⚡Instant API Access

🌐No Setup Required

Google Nano Banana 2 Reference to Image Developer

Nano Banana 2 Reference to Image Developer (Gemini 3.1 Flash Image) is Google's advanced AI-powered video-to-image generation model, designed to generate high-quality static images from video clips combined with text instructions. Built on the same cutting-edge model as Nano Banana 2 Edit, it adds the ability to use video content as a rich reference source — extracting visual context, themes, and key frames to synthesize new images with precision and semantic awareness.

This is the developer-tier variant of Nano Banana 2 Reference to Image, offering a streamlined parameter set. It is ideal for API integrations and workflows where output format flexibility and per-frame media resolution control are not required.

Why Choose This?

Video as reference — Provide a video clip (HTTP URL or YouTube URL) and let the model extract its visual context to guide image generation.
Multi-image reference — Optionally upload up to 10 additional reference images to complement the video input for complex compositions.
Natural language control — Describe exactly what you want with a text prompt; the model understands context, themes, and relationships from both the video and text.
Thinking levels — Choose how much internal reasoning the model applies — higher thinking levels improve quality on complex tasks.
Web search grounding — Optionally enable real-time web search to enrich generation with current information.
Multi-resolution output — Generate at 1K, 2K, or 4K resolution.
Flexible aspect ratios — Multiple options including 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9.

How It Works

The model analyzes your video clip by sampling frames at the specified FPS rate, then interprets the visual content within its multimodal context window. Combined with your text prompt and any additional reference images, it synthesizes a new image grounded in the video's themes, style, and key visual elements. This makes it especially powerful for creating content that is visually consistent with existing video assets.

Parameters

Core Inputs

Parameter	Required	Description
prompt	Yes	Text description of the desired output image
video_clips	Yes	Source video clip(s) for reference generation (max: 1, see below)
images	No	Additional reference images (max: 10, click "+ Add Item" to add more)

Video Clip Fields

Field	Required	Description
url	Yes	URL of the source video clip. Supports HTTP URL or YouTube video URL. HTTP video is limited to 15MB.
start	Yes	Start time in seconds for trimming the video clip (min: 0)
ends	Yes	End time in seconds for trimming the video clip. Set 0 to use the whole video.
fps	Yes	Frame sampling rate (FPS) of the video clip. Range: 0–24. Lower values reduce token usage.

Output Options

Parameter	Required	Description
aspect_ratio	No	Aspect ratio: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
resolution	No	Output resolution: 1k (default), 2k, 4k

Advanced Options

Parameter	Required	Description
thinking_level	No	Reasoning depth: default, high, minimal. Higher levels improve quality on complex tasks but increase latency.
enable_web_search	No	If enabled, grounds generation with real-time web information.

How to Use

Provide a video clip — enter the video URL (HTTP or YouTube) and set start/end times and FPS sampling rate.
Write your prompt — describe the output image clearly (e.g., "Create a cinematic poster based on the key scenes in this video").
Add reference images (optional) — upload additional images to guide composition or style.
Choose aspect ratio (optional) — select a preset or leave empty for default.
Select resolution — choose 1K, 2K, or 4K based on your quality needs.
Adjust advanced settings (optional) — set thinking level or enable web search grounding.
Run — submit and download your generated image.

Pricing

The total cost is determined by the output image resolution multiplied by the number of output images, plus optional per-request fees for video clip input and web search grounding.

SKU Prices

SKU	Description	Unit Price
sku_1k	1K resolution output image	$0.08
sku_2k	2K resolution output image	$0.12
sku_4k	4K resolution output image	$0.16
sku_video_clip	Video clip input (per request)	$0.07
sku_web_search	Web search grounding (per request)	$0.014

Pricing Formula

cost = (resolution == "2k" ? sku_2k : (resolution == "4k" ? sku_4k : sku_1k)) * images
     + (enable_web_search ? sku_web_search : 0)
     + (len(video_clips) > 0 ? sku_video_clip : 0)

Examples:

Resolution	Video Clip	Web Search	Total Cost
1K	Yes	No	$0.08 +$ 0.07 = $0.15
2K	Yes	No	$0.12 +$ 0.07 = $0.19
4K	Yes	No	$0.16 +$ 0.07 = $0.23
1K	Yes	Yes	$0.08 +$ 0.07 + $0.014 = $ 0.164
1K	No	No	$0.08
2K	No	No	$0.12
4K	No	No	$0.16

The video clip fee ( $0.07) and web search fee ($ 0.014) are each charged once per request when the respective feature is enabled, regardless of content volume.

Best Use Cases

Video Thumbnail Generation — Automatically create compelling thumbnails that reflect the video's content and mood.
Promotional Posters — Generate movie-style or campaign posters grounded in actual video footage.
Scene Summarization Art — Create visual summaries or highlight artwork from long-form video content.
Brand Content Creation — Produce consistent image assets from brand video campaigns.
Educational Infographics — Transform instructional videos into static visual materials.
Social Media Assets — Generate platform-optimized images (vertical, square, landscape) from video content.

Pro Tips

Use low FPS (0.5–1) for long videos to keep token usage within limits while still capturing key frames.
Set precise start/end times to focus the model on the most relevant segment of your video.
Combine specific text prompts with the video input — vague prompts may produce generic results.
Add reference images alongside the video to guide composition style more precisely.
Use thinking_level: high for complex scene interpretations or when visual fidelity matters most.
YouTube URLs are supported directly — no need to download and re-upload public videos.
2K offers excellent quality at a reasonable price — only $0.04 more than 1K per image.
If you need output_format (PNG/JPEG) or media_resolution control, use the standard Reference to Image model instead.

Notes

Both prompt and video_clips are required fields.
Maximum video clips: 1 per request.
HTTP video URLs are limited to 15MB; use YouTube URLs for larger videos.
Maximum additional reference images: 10.
FPS range: 0–24. Higher FPS captures more frames but consumes more tokens.
The video clip fee ($0.07) is a flat per-request charge, not per frame or per second.
Output format is not configurable in this variant; use the standard model if PNG/JPEG selection is required.
Ensure your content and prompts comply with Google's Safety Guidelines.

Nano Banana 2 Reference to Image — Full-featured variant with output_format, media_resolution, and enable_image_search support.
Nano Banana 2 Edit — Edit images using text prompts and reference images (no video input).
Nano Banana 2 Text-to-Image — Generate images from text prompts only.
Nano Banana Pro Edit — Pro tier editing with enhanced quality.
Nano Banana Pro Text-to-Image — Pro tier image generation.

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Nano Banana 2 Reference-to-Image Developer API by Google

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

Please log in to view request history

What's New in Nano Banana 2

Prompt Examples & Templates

Marketing Mockup with Text

Multi-Scene Character

Person to Action Figure

Real-World Reference Generation

Product Design Render

Artistic Style Transformation

Ultra High Resolution Scene

Use Cases

Why Choose Nano Banana 2?

Flash Speed

Near-Pro Quality

Cost Effective

Technical Specifications

Experience Nano Banana 2

Google Nano Banana 2 Reference to Image Developer

Why Choose This?

How It Works

Parameters

Core Inputs

Video Clip Fields

Output Options

Advanced Options

How to Use

Pricing

SKU Prices

Pricing Formula

Best Use Cases

Pro Tips

Notes

Related Models

Explore Similar Models

Nano Banana 2 Reference to Image

Nano Banana 2 Text-to-Image

Nano Banana 2 Edit

Nano Banana Pro Text-to-image Ultra

Nano Banana Pro Edit Ultra

Nano Banana Pro Text-to-image

Nano Banana Pro Edit

Nano Banana Text-to-image

Nano Banana Edit

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Imagen4 Fast