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

Bild-zu-Bild

Nano Banana 2 Reference-to-Image API by Google

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

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.

Eingabe

Parameterkonfiguration wird geladen...

Ausgabe

Inaktiv

Ihre generierten Bilder erscheinen hier

Konfigurieren Sie Parameter und klicken Sie auf Ausführen, um mit der Generierung zu beginnen

Jede Ausführung kostet $0.08. Für $10 können Sie ca. 125 Mal ausführen.

Sie können fortfahren mit:

Bild zu Video Bild zu Bild

Parameter

Codebeispiel
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",
    "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()

Installieren

Installieren Sie das erforderliche Paket für Ihre Programmiersprache.

pip install requests

Authentifizierung

Alle API-Anfragen erfordern eine Authentifizierung über einen API-Schlüssel. Sie können Ihren API-Schlüssel über das Atlas Cloud Dashboard erhalten.

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

HTTP-Header

import os

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

Schützen Sie Ihren API-Schlüssel

Geben Sie Ihren API-Schlüssel niemals in clientseitigem Code oder öffentlichen Repositories preis. Verwenden Sie stattdessen Umgebungsvariablen oder einen Backend-Proxy.

Anfrage senden

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())

Anfrage senden

Senden Sie eine asynchrone Generierungsanfrage. Die API gibt eine Vorhersage-ID zurück, mit der Sie den Status prüfen und das Ergebnis abrufen können.

POST/api/v1/model/generateImage

Anfragekörper

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",
    "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']}")

Antwort

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

Status prüfen

Fragen Sie den Vorhersage-Endpunkt ab, um den aktuellen Status Ihrer Anfrage zu überprüfen.

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

Abfrage-Beispiel

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)

Statuswerte

processingDie Anfrage wird noch verarbeitet.

completedDie Generierung ist abgeschlossen. Ergebnisse sind verfügbar.

succeededDie Generierung war erfolgreich. Ergebnisse sind verfügbar.

failedDie Generierung ist fehlgeschlagen. Überprüfen Sie das Fehlerfeld.

Abgeschlossene Antwort

{
  "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"
  }
}

Dateien hochladen

Laden Sie Dateien in den Atlas Cloud Speicher hoch und erhalten Sie eine URL, die Sie in Ihren API-Anfragen verwenden können. Verwenden Sie multipart/form-data zum Hochladen.

POST/api/v1/model/uploadMedia

Upload-Beispiel

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}")

Antwort

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

Eingabe-Schema

Die folgenden Parameter werden im Anfragekörper akzeptiert.

Gesamt: 0Erforderlich: 0Optional: 0

Keine Parameter verfügbar.

Beispiel-Anfragekörper

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

Ausgabe-Schema

Die API gibt eine Vorhersage-Antwort mit den generierten Ausgabe-URLs zurück.

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

Beispielantwort

{
  "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 integriert über 300 KI-Modelle direkt in Ihren KI-Coding-Assistenten. Ein Befehl zur Installation, dann verwenden Sie natürliche Sprache, um Bilder, Videos zu generieren und mit LLMs zu chatten.

Unterstützte Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ unterstützte clients

Installieren

npx skills add AtlasCloudAI/atlas-cloud-skills

API-Schlüssel einrichten

Erhalten Sie Ihren API-Schlüssel über das Atlas Cloud Dashboard und setzen Sie ihn als Umgebungsvariable.

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

Funktionen

Nach der Installation können Sie natürliche Sprache in Ihrem KI-Assistenten verwenden, um auf alle Atlas Cloud Modelle zuzugreifen.

BildgenerierungGenerieren Sie Bilder mit Modellen wie Nano Banana 2, Z-Image und mehr.

VideoerstellungErstellen Sie Videos aus Text oder Bildern mit Kling, Vidu, Veo usw.

LLM-ChatChatten Sie mit Qwen, DeepSeek und anderen großen Sprachmodellen.

Medien-UploadLaden Sie lokale Dateien für Bildbearbeitung und Bild-zu-Video-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-Server

Der Atlas Cloud MCP-Server verbindet Ihre IDE mit über 300 KI-Modellen über das Model Context Protocol. Funktioniert mit jedem MCP-kompatiblen Client.

Unterstützte Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ unterstützte clients

Installieren

npx -y atlascloud-mcp

Konfiguration

Fügen Sie die folgende Konfiguration zur MCP-Einstellungsdatei Ihrer IDE hinzu.

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

Verfügbare Werkzeuge

atlas_generate_imageGenerieren Sie Bilder aus Textbeschreibungen.

atlas_generate_videoErstellen Sie Videos aus Text oder Bildern.

atlas_chatChatten Sie mit großen Sprachmodellen.

atlas_list_modelsDurchsuchen Sie über 300 verfügbare KI-Modelle.

atlas_quick_generateInhaltserstellung in einem Schritt mit automatischer Modellauswahl.

atlas_upload_mediaLaden Sie lokale Dateien für API-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/mcp-server

API-Schema

Schema nicht verfügbar

Keine Beispiele verfügbar

Anmelden, um Anfrageverlauf anzuzeigen

Sie müssen angemeldet sein, um auf Ihren Modellanfrageverlauf zuzugreifen.

Anmelden

Bildgenerierung der nächsten Generation

Ausgabe bis zu 4K Auflösung (512px / 1K / 2K / 4K Stufen)
10+ Seitenverhältnisse einschließlich 21:9, 1:4, 8:1 und mehr
Präzises und lesbares Text-Rendering in Bildern
Nahezu Pro-Qualität (~95%) bei Flash-Geschwindigkeit

Intelligente Bearbeitung und Konsistenz

Charakterkonsistenz für bis zu 5 Charaktere über Szenen hinweg
Objekttreue für bis zu 14 Objekte in einem Workflow
Gezielte Bearbeitungen über natürliche Sprache (entfernen, ersetzen, umposieren)
Multi-Bild-Blending und nahtlose Komposition

Was ist neu in Nano Banana 2

3-5x schneller als Pro

Aufgebaut auf der Gemini 3.1 Flash Architektur generiert Nano Banana 2 Standardbilder in 4-8 Sekunden — verglichen mit 10-20 Sekunden bei Nano Banana Pro.

Bildsuch-Grounding

Das herausragende Feature von NB2 — es kann während der Generierung reale Referenzbilder über die Google-Suche abrufen und so die Genauigkeit für Sehenswürdigkeiten, berühmte Personen und Markenlogos erheblich verbessern.

Präzises Text-Rendering

Generieren Sie präzisen, lesbaren Text für Marketing-Mockups, Grußkarten und lokalisierte Inhalte. Sie können sogar Text innerhalb eines Bildes übersetzen und lokalisieren.

Multi-Charakter-Konsistenz

Behalten Sie die visuelle Konsistenz für bis zu 5 Charaktere und 14 Objekte über Szenen hinweg bei — perfekt für Storyboards, Comics und Marketingkampagnen.

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

Anwendungsfälle

🎬

Storyboarding und Comics

📸

Produktfotografie

📊

Marketing-Mockups

📱

Social-Media-Inhalte

🔤

Text-Overlay-Design

👤

Charakter-Design

✨

Fotobearbeitung und Retusche

🎨

Visuelle Markeninhalte

Warum Nano Banana 2 wählen?

⚡

Flash-Geschwindigkeit

3-5x schneller als Nano Banana Pro mit 4-8 Sekunden Standard-Generierungszeit

🎯

Nahezu Pro-Qualität

Erreicht in den meisten Szenarien ungefähr 95% der Bildqualität von Pro

💰

Kosteneffizient

Ungefähr die Hälfte der Kosten von Nano Banana Pro — hochwertige KI-Bildgenerierung wird zugänglicher

Technische Spezifikationen

Architektur:Gemini 3.1 Flash (GEMPIX2)

Auflösungsunterstützung:512px bis 4K (512px / 1K / 2K / 4K Stufen)

Seitenverhältnisse:1:1, 4:3, 3:4, 2:3, 3:2, 16:9, 9:16, 1:4, 4:1, 8:1, 21:9

Konsistenz:Bis zu 5 Charaktere + 14 Objekte pro Workflow

Inhaltssicherheit:SynthID-Wasserzeichen, C2PA-Standard kompatibel

API-Zugang:Gemini API, Vertex AI, AI Studio, Gemini CLI

Erleben Sie Nano Banana 2

Pro-Level-Bildgenerierung in Flash-Geschwindigkeit — erstellen Sie beeindruckende Visuals mit Charakterkonsistenz, Text-Rendering und 4K-Auflösungsunterstützung.

✨Kostenlose Credits zum Start

⚡Sofortiger API-Zugang

🌐Keine Einrichtung erforderlich

Google Nano Banana 2 Reference to Image

Nano Banana 2 Reference to Image (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 model is ideal for creating thumbnails, posters, promotional artwork, and scene summaries by leveraging the visual richness of existing video content alongside natural language guidance.

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.
Media resolution control — Balance detail and token usage for input video frames with LOW, MEDIUM, or HIGH media resolution modes.
Web & image search grounding — Optionally enable real-time web or image 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.
Format choice — Export in PNG or JPEG format.

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
output_format	No	Output format: png (default), jpeg

Advanced Options

Parameter	Required	Description
thinking_level	No	Reasoning depth: default, high, minimal. Higher levels improve quality on complex tasks but increase latency.
media_resolution	No	How input media frames are processed: default, low, medium, high. LOW reduces tokens per frame, allowing longer videos.
enable_web_search	No	If enabled, grounds generation with real-time web information.
enable_image_search	No	If enabled, grounds generation with real-time image search results.

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.
Choose output format — PNG for transparency support, JPEG for smaller file size.
Adjust advanced settings (optional) — set thinking level, media resolution, or enable 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, web search, and image 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
sku_image_search	Image 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)
     + (enable_image_search ? sku_image_search : 0)
     + (len(video_clips) > 0 ? sku_video_clip : 0)

Examples:

Resolution	Video Clip	Web Search	Image Search	Total Cost
1K	Yes	No	No	$0.08 +$ 0.07 = $0.15
2K	Yes	No	No	$0.12 +$ 0.07 = $0.19
4K	Yes	No	No	$0.16 +$ 0.07 = $0.23
1K	Yes	Yes	No	$0.08 +$ 0.07 + $0.014 = $ 0.164
1K	Yes	Yes	Yes	$0.08 +$ 0.07 + $0.014 +$ 0.014 = $0.178
1K	No	No	No	$0.08
2K	No	No	No	$0.12
4K	No	No	No	$0.16

The video clip fee ( $0.07), web search fee ($ 0.014), and image 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.
Set media_resolution: low when analyzing long videos to allow more frames within the context window.
2K offers excellent quality at a reasonable price — only $0.04 more than 1K per image.
YouTube URLs are supported directly — no need to download and re-upload public videos.

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.
If aspect_ratio is not selected, the model uses a default ratio.
4K resolution costs 2× the standard 1K rate.
Ensure your content and prompts comply with Google's Safety Guidelines.

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.

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

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

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.

Nano Banana Pro Edit

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 Text-to-image

Google's state-of-the-art image generation and editing model.

Nano Banana Edit

Google's state-of-the-art image generation and editing model.

Imagen3

Google's highest quality text-to-image model, capable of generating images with detail, rich lighting and beauty.

Image3 Fast

Google's highest quality text-to-image model, capable of generating images with detail, rich lighting and beauty.

Imagen4 Fast

Google's Imagen 4 flagship model.

Imagen4 Ultra

Google’s highest quality image generation model.

Imagen4

Google's Imagen 4 flagship model.

Openai GPT Image 2 Text-to-Image

GPT Image 2 text to image is OpenAI's fast, cost-efficient text-to-image generator powered by GPT-5 guidance. Create photorealistic shots, product renders, concept art, and stylized graphics from natural-language prompts (optionally conditioned with an image). Supports custom aspect ratios, seeds, negative prompts, hex color hints, and style presets. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Openai GPT Image 2 Edit

GPT Image 2 Edit is OpenAI's image model for precise, natural-language edits. Add/remove objects, swap backgrounds, retouch faces, adjust colors/lighting, edit text/graphics, crop/resize, and apply hex color control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

From

$0.01/BILD

Eine API für alle Media-KI.

Alle Modelle erkunden

Nano Banana 2 Reference-to-Image API by Google

Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel

Statuswerte

Abgeschlossene Antwort

Dateien hochladen

Upload-Beispiel

Antwort

Eingabe-Schema

Beispiel-Anfragekörper

Ausgabe-Schema

Beispielantwort

Atlas Cloud Skills

Unterstützte Clients

Installieren

API-Schlüssel einrichten

Funktionen

MCP-Server

Unterstützte Clients

Installieren

Konfiguration

Verfügbare Werkzeuge

API-Schema

Anmelden, um Anfrageverlauf anzuzeigen

Was ist neu 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

Anwendungsfälle

Warum Nano Banana 2 wählen?

Flash-Geschwindigkeit

Nahezu Pro-Qualität

Kosteneffizient

Technische Spezifikationen

Erleben Sie Nano Banana 2

Google Nano Banana 2 Reference to Image

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

Ähnliche Modelle Erkunden

Nano Banana 2 Reference to Image Developer

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

Imagen3

Image3 Fast

Imagen4 Fast