Generate and edit videos with Gemini Omni Flash

Gemini Omni Flash (gemini-omni-1.1-flash) is a high-performance multimodal model designed for high-speed video generation, editing, and cinematic control. Gemini Omni is built on the following core capabilities that distinguish it from previous video models:

  • Native multimodality: it processes text, image, audio, and video simultaneously, giving you more cohesive, consistent, and controllable output.
  • Conversational editing: enabled by the Interactions API, it lets you iteratively refine and edit your videos through natural language conversation. Describe what you want to change, and the model applies the edit while preserving the parts of the video you want to keep.
  • World knowledge: Gemini Omni combines an understanding of physics with Gemini's knowledge of history, science, and cultural context, bridging the gap from photorealism to meaningful storytelling.

Text to video generation

Generate a video from a text prompt. The model generates a video with audio based on your text description. Write prompts with details like scene description, camera movement, lighting and mood for best results.

Python

import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    input="A marble rolling fast on a chain reaction style track, continuous smooth shot."
)
with open("marble.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';
const ai = new GoogleGenAI({});

const interaction = await ai.interactions.create({
  model: 'gemini-omni-1.1-flash',
  input: 'A marble rolling fast on a chain reaction style track, continuous smooth shot.',
});

if (interaction.output_video?.data) {
  fs.writeFileSync('marble.mp4', Buffer.from(interaction.output_video.data, 'base64'));
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Base64;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-omni-1.1-flash"))
        .input(InteractionsInput.of("A marble rolling fast on a chain reaction style track, continuous smooth shot."))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.outputVideo().isPresent() && interaction.outputVideo().get().data().isPresent()) {
    byte[] videoBytes = Base64.getDecoder().decode(interaction.outputVideo().get().data().get());
    Files.write(Paths.get("marble.mp4"), videoBytes);
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions?key=$API_KEY" \
-H "Content-Type: application/json" \
-d '{
 "model": "gemini-omni-1.1-flash",
 "input": "A marble rolling fast on a chain reaction style track, continuous smooth shot."
}'

REST response schema

The convenience field interaction.output_video is SDK-only. Get the video output from the steps array when using the REST API directly.

Raw REST JSON structure:

{
  "steps": [
    { "type": "user_input", "content": [{"type": "text", "text": "..."}] },
    { "type": "thought", "content": [{"text": "...", "type": "thought"}] },
    {
      "type": "model_output",
      "content": [
        {
          "type": "video",
          "mime_type": "video/mp4",
          "data": "AAAAIGZ0eXBpc29t..." // Base64 encoded video data
        }
      ]
    }
  ],
  "id": "v1_...",
  "status": "completed",
  "model": "gemini-omni-1.1-flash",
  "object": "interaction"
}

Control aspect ratio

Set the aspect_ratio to "9:16" to create portrait videos. Landscape (16:9) is the default.

Python

import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    input="A futuristic city with neon lights and flying cars, cyberpunk style",
    response_format={
        "type": "video",  # optional
        "aspect_ratio": "9:16"  # Supported values: "9:16", "16:9"
    }
)
with open("example.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';
const ai = new GoogleGenAI({});

const interaction = await ai.interactions.create({
  model: 'gemini-omni-1.1-flash',
  input: 'A futuristic city with neon lights and flying cars, cyberpunk style',
  response_format: {
    type: 'video', // optional
    aspect_ratio: '9:16' // Supported values: '9:16', '16:9'
  },
});

if (interaction.output_video?.data) {
  fs.writeFileSync('example.mp4', Buffer.from(interaction.output_video.data, 'base64'));
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.VideoResponseFormat;
import com.google.genai.gaos.models.interactions.VideoResponseFormatAspectRatio;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Base64;

Client client = new Client();

VideoResponseFormat videoFormat =
    VideoResponseFormat.builder()
        .aspectRatio(VideoResponseFormatAspectRatio.of("9:16"))
        .build();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-omni-1.1-flash"))
        .input(InteractionsInput.of("A futuristic city with neon lights and flying cars, cyberpunk style"))
        .responseFormat(CreateModelInteractionResponseFormat.of(ResponseFormat.of(videoFormat)))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.outputVideo().isPresent() && interaction.outputVideo().get().data().isPresent()) {
    byte[] videoBytes = Base64.getDecoder().decode(interaction.outputVideo().get().data().get());
    Files.write(Paths.get("example.mp4"), videoBytes);
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions?key=$API_KEY" \
-H "Content-Type: application/json" \
-d '{
 "model": "gemini-omni-1.1-flash",
 "input": "A futuristic city with neon lights and flying cars, cyberpunk style",
 "response_format": {
   "type": "video",
   "aspect_ratio": "9:16"
 }
}'

Output resolution

Control the output resolution of your generated video using the resolution parameter in response_format. The default resolution is 720p.

Value Description
360p 360p output resolution
720p 720p output resolution (default)
1080p 1080p output (upscaled)
4k 4K output (upscaled)

Python

import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    input="A drone shot of a mountain landscape at sunrise.",
    response_format={
        "type": "video",
        "resolution": "1080p",
    },
)
with open("hires.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';
const ai = new GoogleGenAI({});

const interaction = await ai.interactions.create({
  model: 'gemini-omni-1.1-flash',
  input: 'A drone shot of a mountain landscape at sunrise.',
  response_format: {
    type: 'video',
    resolution: '1080p',
  },
});

if (interaction.output_video?.data) {
  fs.writeFileSync('hires.mp4', Buffer.from(interaction.output_video.data, 'base64'));
}

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.Resolution;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.VideoResponseFormat;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Base64;

Client client = new Client();

VideoResponseFormat videoFormat =
    VideoResponseFormat.builder()
        .resolution(Resolution.of("1080p"))
        .build();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-omni-1.1-flash"))
        .input(InteractionsInput.of("A drone shot of a mountain landscape at sunrise."))
        .responseFormat(CreateModelInteractionResponseFormat.of(ResponseFormat.of(videoFormat)))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

if (interaction.outputVideo().isPresent() && interaction.outputVideo().get().data().isPresent()) {
    byte[] videoBytes = Base64.getDecoder().decode(interaction.outputVideo().get().data().get());
    Files.write(Paths.get("hires.mp4"), videoBytes);
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions?key=$API_KEY" \
-H "Content-Type: application/json" \
-d '{
 "model": "gemini-omni-1.1-flash",
 "input": "A drone shot of a mountain landscape at sunrise.",
 "response_format": {
   "type": "video",
   "resolution": "1080p"
 }
}'

Image to video generation

You can provide a reference image with your text prompt. Depending on your prompt, the model will decide how to use the image. This is useful for bringing product shots, illustrations, or photographs to life.

The following example shows how to use the reference image of a drawing of a fish jumping out of water:

Drawing of a fish jumping out of water

With the following prompt:

turn this into realistic footage, using the drawing only as a guide for movement, do not show the drawing in the final video

To generate a realistic video of the drawing.

Python

import base64
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    input=[
        {"type": "image", "data": base64_image, "mime_type": "image/jpeg"},
        {"type": "text", "text": "turn this into realistic footage, using the drawing only as a guide for movement, do not show the drawing in the final video"}
    ],
)
with open("clownfish.mp4", "wb") as f:
    f.write(base64.b64decode(interaction.output_video.data))

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';
const ai = new