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.
What it does
Nano Banana 2 Reference to Image Developer, in practice.
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 contr
- 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.
Run Nano Banana 2 Reference to Image Developer
from $0.060/imageParameters
What you can set.
| Parameter | What it does | Options |
|---|---|---|
aspect_ratio | The aspect ratio of the generated media. | 1:1 3:2 2:3 3:4 4:3 4:5 5:4 9:16 16:9 21:9 |
video_clips | Source video clips to use as references for generation. Supports 1 video clip. | |
enable_web_search | If enabled, the model will use web search to ground the generation with real-time information. | default |
images | List of URLs of input images for editing. The maximum number of images is 10. | |
prompt | The positive prompt for the generation. | |
resolution | The resolution of the output image. | 1k 2k 4k |
thinking_level | Controls the amount of internal reasoning the model performs before generating a response. Higher levels may improve quality on complex tasks but increase latency. | default high minimal |
seed | Random seed. Does not guarantee determinism but may improve repeatability. -1 means a random seed will be used. | default -1 |
top_p | Probability threshold for top-p sampling | default 0.95 |
temperature | Creativity allowed in the responses. Best results at default 1.0. Lower values may impact reasoning. | default 1 |
Sample prompt
The prompt behind the sample.
Integrate the car from the image into the video's environment.
enable_base64_output: enable_sync_mode: enable_web_search: resolution: 2kthinking_level: defaultaspect_ratio: 16:9FAQ
Short answers.
How much does Nano Banana 2 Reference to Image Developer cost?
Pricing starts at $0.060 per image at the base resolution; higher resolutions and longer durations cost more. Usage is billed per request from your balance — no subscription.
Does Nano Banana 2 Reference to Image Developer run uncensored here?
No. Google applies its own content policy to this model regardless of where it is called from. For a route without an extra platform filter use Uncensored MiniMax H3; this page lists Nano Banana 2 Reference to Image Developer for everything else it does well.
What does Nano Banana 2 Reference to Image Developer take as input?
It is a image editing model. Google's advanced AI-powered video-to-image generation model, designed to generate high-quality static images from video clips combined with text instructions.
How do I use it?
Enter an invite code to open Chat with the model loaded, or join the waitlist. We open seats in batches and track which models are requested most.
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