google/gemini-omni-flash/text-to-video

text-till-video

Gemini Omni Flash Text-to-Video API by Google

google/gemini-omni-flash/text-to-video

Text-to-video

A natively multimodal Google DeepMind model that generates cinematic videos with synchronized native audio from a text prompt alone, grounded in real-world physics for controllable, high-speed video generation.

Inmatning

Prompt *

Varaktighet

Bildförhållande

Thinking level

Upplösning

Seed

Utmatning

Vilande

Dina genererade videor visas här

Konfigurera parametrar och klicka på Kör för att börja generera

Varje körning kostar $0.125. För $10 kan du köra cirka 80 gånger.

Du kan fortsätta med:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parametrar

Kodexempel
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "google/gemini-omni-flash/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. Text prompt for generation
    "duration": 10,  # The duration of the generated video in seconds. (min: 3, max: 10)
    "aspect_ratio": "16:9",  # The aspect ratio of the generated video. options: 16:9 | 9:16
    "resolution": "720p",  # The resolution of the generated video. options: 720p
    "thinking_level": "default",  # Controls the amount of internal reasoning the model performs before generating a response. options: default | high | low
    "seed": -1,  # The random seed to use for the generation
}

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"] in ["completed", "succeeded"]:
            print("Generated video:", 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)

video_url = check_status()

Installera

Installera det nödvändiga paketet för ditt programmeringsspråk.

pip install requests

Autentisering

Alla API-förfrågningar kräver autentisering via en API key. Du kan hämta din API key från Atlas Cloud-instrumentpanelen.

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

HTTP-huvuden

import os

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

Håll din API key säker

Exponera aldrig din API key i klientkod eller publika arkiv. Använd miljövariabler eller en backend-proxy istället.

Skicka en förfrågan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
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())

Skicka en förfrågan

Skicka en asynkron genereringsförfrågan. API:et returnerar ett prediction ID som du kan använda för att kontrollera statusen och hämta resultatet.

POST/api/v1/model/generateVideo

Förfrågningsinnehåll

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "google/gemini-omni-flash/text-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Svar

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Kontrollera status

Polla prediction-endpointen för att kontrollera den aktuella statusen för din förfrågan.

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

Polling-exempel

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)

Statusvärden

processingFörfrågan bearbetas fortfarande.

completedGenereringen är klar. Utdata är tillgängliga.

succeededGenereringen lyckades. Utdata är tillgängliga.

failedGenereringen misslyckades. Kontrollera error-fältet.

Slutfört svar

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Ladda upp filer

Ladda upp filer till Atlas Cloud-lagring och få en URL som du kan använda i dina API-förfrågningar. Använd multipart/form-data för uppladdning.

POST/api/v1/model/uploadMedia

Uppladdningsexempel

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}")

Svar

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

Input Schema

Följande parametrar accepteras i förfrågningsinnehållet.

Totalt: 7Obligatorisk: 2Valfri: 5

modelstringrequired

model name

Default: "google/gemini-omni-flash/text-to-video"

promptstringrequired

Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.

durationinteger

The duration of the generated video in seconds.

Default: 10Min: 3Max: 10

aspect_ratiostring

The aspect ratio of the generated video.

Default: "16:9"

16:99:16

resolutionstring

The resolution of the generated video.

Default: "720p"

720p

thinking_levelstring

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: "default"

defaulthighlow

seedinteger

The random seed to use for the generation. -1 means a random seed will be used.

Default: -1

Exempel på förfrågningsinnehåll

{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": "A beautiful landscape",
  "duration": 10,
  "aspect_ratio": "16:9",
  "resolution": "720p",
  "thinking_level": "default",
  "seed": -1
}

Output Schema

API:et returnerar ett prediction-svar med de genererade utdata-URL:erna.

codeinteger

HTTP status code of the response.

messagestring

Human-readable message; non-empty on failure.

dataobject

Exempelsvar

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrerar 400+ AI-modeller direkt i din AI-kodassistent. Ett kommando för att installera, sedan använd naturligt språk för att generera bilder, videor och chatta med LLM.

Stödda klienter

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ stödda klienter

Installera

npx skills add AtlasCloudAI/atlas-cloud-skills

Konfigurera API Key

Hämta din API key från Atlas Cloud-instrumentpanelen och ställ in den som en miljövariabel.

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

Funktioner

När det är installerat kan du använda naturligt språk i din AI-assistent för att komma åt alla Atlas Cloud-modeller.

BildgenereringGenerera bilder med modeller som Nano Banana 2, Z-Image och fler.

VideoskapandeSkapa videor från text eller bilder med Kling, Vidu, Veo m.fl.

LLM-chattChatta med Qwen, DeepSeek och andra stora språkmodeller.

MediauppladdningLadda upp lokala filer för bildredigering och bild-till-video-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server ansluter din IDE med 400+ AI-modeller via Model Context Protocol. Fungerar med alla MCP-kompatibla klienter.

Stödda klienter

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ stödda klienter

Installera

npx -y atlascloud-mcp

Konfiguration

Lägg till följande konfiguration i din IDE:s MCP-inställningsfil.

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

Tillgängliga verktyg

atlas_generate_imageGenerera bilder från textpromptar.

atlas_generate_videoSkapa videor från text eller bilder.

atlas_chatChatta med stora språkmodeller.

atlas_list_modelsBläddra bland 400+ tillgängliga AI-modeller.

atlas_quick_generateInnehållsskapande i ett steg med automatiskt modellval.

atlas_upload_mediaLadda upp lokala filer för API-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "google/gemini-omni-flash/text-to-video"
          },
          "prompt": {
            "description": "Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.",
            "type": "string"
          },
          "duration": {
            "default": 10,
            "description": "The duration of the generated video in seconds.",
            "type": "integer",
            "maximum": 10,
            "minimum": 3,
            "x-ui-component": "select"
          },
          "aspect_ratio": {
            "default": "16:9",
            "description": "The aspect ratio of the generated video.",
            "enum": [
              "16:9",
              "9:16"
            ],
            "type": "string",
            "x-placeholder": "Select one",
            "x-ui-component": "select"
          },
          "resolution": {
            "default": "720p",
            "description": "The resolution of the generated video.",
            "enum": [
              "720p"
            ],
            "type": "string",
            "x-placeholder": "Select one",
            "x-ui-component": "select"
          },
          "thinking_level": {
            "description": "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": "default",
            "enum": [
              "default",
              "high",
              "low"
            ],
            "type": "string"
          },
          "seed": {
            "default": -1,
            "description": "The random seed to use for the generation. -1 means a random seed will be used.",
            "type": "integer"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "duration",
          "aspect_ratio",
          "thinking_level",
          "resolution",
          "seed"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "code": {
            "description": "HTTP status code of the response.",
            "type": "integer"
          },
          "message": {
            "description": "Human-readable message; non-empty on failure.",
            "type": "string"
          },
          "data": {
            "type": "object",
            "properties": {
              "id": {
                "description": "Unique identifier for the prediction.",
                "type": "string"
              },
              "model": {
                "description": "Model ID used for the prediction.",
                "type": "string"
              },
              "outputs": {
                "description": "Array of URLs to the generated content. Null when status is not completed.",
                "type": "array",
                "items": {
                  "type": "string"
                },
                "nullable": true
              },
              "urls": {
                "description": "Object containing related API endpoints.",
                "type": "object",
                "properties": {
                  "get": {
                    "description": "URL to poll for the prediction result.",
                    "type": "string",
                    "format": "uri"
                  }
                }
              },
              "has_nsfw_contents": {
                "description": "Array of boolean values indicating NSFW detection for each output. Null if not applicable.",
                "type": "array",
                "items": {
                  "type": "boolean"
                },
                "nullable": true
              },
              "status": {
                "description": "Status of the task: created, processing, completed, timeout, or failed.",
                "type": "string"
              },
              "created_at": {
                "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
                "format": "date-time",
                "type": "string"
              },
              "error": {
                "description": "Error message if the task failed, empty string otherwise.",
                "type": "string"
              },
              "error_code": {
                "description": "Error code if the task failed.",
                "type": "integer"
              },
              "executionTime": {
                "description": "Total execution time in milliseconds.",
                "type": "number"
              },
              "timings": {
                "description": "Detailed timing breakdown.",
                "type": "object",
                "properties": {
                  "inference": {
                    "description": "Inference time in milliseconds.",
                    "type": "number"
                  }
                }
              }
            }
          }
        }
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM-vänlig Promptmall

# google/gemini-omni-flash/text-to-video

> A natively multimodal Google DeepMind model that generates cinematic videos with synchronized native audio from a text prompt alone, grounded in real-world physics for controllable, high-speed video generation.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — 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/gemini-omni-flash/text-to-video`


## 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/gemini-omni-flash/text-to-video"`

- **`prompt`** (`string`, _required_):
  Text prompt for generation. Describes the target content, style, camera language, or character actions. Maximum 20,000 characters.

- **`duration`** (`integer`, _optional_):
  The duration of the generated video in seconds.
  - Default: `10`
  - Min: 3
  - Max: 10

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the generated video.
  - Default: `"16:9"`
  - Options: "16:9", "9:16"

- **`thinking_level`** (`string`, _optional_):
  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: `"default"`
  - Options: "default", "high", "low"

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"720p"`
  - Options: "720p"

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation. -1 means a random seed will be used.
  - Default: `-1`



**Required Parameters Example**:

```json
{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": "",
  "duration": 10,
  "aspect_ratio": "16:9",
  "thinking_level": "default",
  "resolution": "720p",
  "seed": -1
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):
  HTTP status code of the response.

- **`message`** (`string`, _optional_):
  Human-readable message; non-empty on failure.

- **`data`** (`object`, _optional_):
  - Properties:
    - **`id`** (`string`, _optional_):
      Unique identifier for the prediction.

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

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

    - **`urls`** (`object`, _optional_):
      Object containing related API endpoints.
      - Properties:
        - **`get`** (`string`, _optional_):
          URL to poll for the prediction result.


    - **`has_nsfw_contents`** (`array[boolean]`, _optional_):
      Array of boolean values indicating NSFW detection for each output. Null if not applicable.

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

    - **`created_at`** (`string`, _optional_):
      ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

    - **`error`** (`string`, _optional_):
      Error message if the task failed, empty string otherwise.

    - **`error_code`** (`integer`, _optional_):
      Error code if the task failed.

    - **`executionTime`** (`number`, _optional_):
      Total execution time in milliseconds.

    - **`timings`** (`object`, _optional_):
      Detailed timing breakdown.
      - Properties:
        - **`inference`** (`number`, _optional_):
          Inference time in milliseconds.





**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": {
    "id": "",
    "model": "",
    "outputs": [
      ""
    ],
    "urls": {
      "get": ""
    },
    "has_nsfw_contents": [],
    "status": "",
    "created_at": "",
    "error": "",
    "error_code": 0,
    "executionTime": 0,
    "timings": {
      "inference": 0
    }
  }
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/gemini-omni-flash/text-to-video",
  "prompt": "",
  "duration": 10,
  "aspect_ratio": "16:9",
  "thinking_level": "default",
  "resolution": "720p",
  "seed": -1
}'

# 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/gemini-omni-flash/text-to-video)

A solitary man leans against a vintage red coupe on an empty seaside road at dusk. The wind gently moves the grass and his coat while a lonely cloud hangs motionless above him. The camera slowly tracks sideways, capturing reflections on the car body and the endless blue ocean beyond.

Laddar...

Gemini Omni Flash — Text to Video

Model ID: google/gemini-omni-flash/text-to-video

Gemini Omni Flash is Google DeepMind's high-performance, natively multimodal model built for high-speed video generation, editing, and cinematic control. This variant accepts a text prompt only, making it ideal for pure creative generation where you describe the entire scene through language.

Overview

Gemini Omni Flash (gemini-omni-flash-preview) was introduced by Google alongside Nano Banana 2 Lite as a new generation of multimodal media models. Unlike traditional pipelines that stitch modalities together, Omni Flash is a single transformer that processes text, images, audio, and video simultaneously, producing output that is more cohesive, consistent, and controllable.

What sets it apart from earlier video models (such as the Veo family) is that it natively generates audio with every video — dialogue, ambience, music, and sound design are produced together with the picture rather than added afterward. The model is grounded in Gemini's real-world knowledge, so it reasons about physics, narrative logic, culture, and visual composition to produce results that feel intentional and cinematic. Generated media carries an invisible SynthID watermark.

AtlasCloud exposes Gemini Omni Flash through four endpoints — text-to-video, image-to-video, reference-to-video, and video-edit. All four route to the same gemini-omni-flash-preview model and differ only by the input modality they accept, corresponding to the model's task parameter (text_to_video, image_to_video, reference_to_video, edit). This endpoint maps to text_to_video.

Inputs

This variant takes a text prompt as its only content input. You describe the subjects, actions, camera language, lighting, mood, style, and any dialogue or sound design entirely in natural language, and the model synthesizes a video (with audio) from scratch.

Key Capabilities

Rich prompt understanding — Describe subjects, actions, camera movements, lighting, mood, style, and audio in a single prompt of up to 20,000 characters.
Native audio generation — Every clip is rendered with a synchronized soundtrack (speech, music, effects) driven by your description.
World-grounded realism — Physics, motion, and scene dynamics informed by Gemini's real-world knowledge.
Cinematic control — Camera framing, pacing, and single-scene composition guided directly from the prompt.
Adjustable reasoning — The thinking_level control trades latency for quality on complex prompts.
Reproducible results — Set a fixed seed to reproduce or iterate on a specific generation.

Input Parameters

Parameter	Type	Required	Default	Description
`model`	string	Yes	`google/gemini-omni-flash/text-to-video`	Model identifier
`prompt`	string	Yes	—	Text description of the video. Max 20,000 characters.
`duration`	integer	No	`10`	Video length in seconds. Range: `3`–`10`.
`aspect_ratio`	string	No	`16:9`	Output aspect ratio. Enum: `16:9`, `9:16`.
`thinking_level`	string	No	`default`	Internal reasoning effort. Enum: `default`, `high`, `low`.
`resolution`	string	No	`720p`	Output resolution. Enum: `720p`.
`seed`	integer	No	`-1`	Random seed for reproducibility. `-1` uses a random seed.

Use Cases

Creative storytelling — Generate cinematic scenes with sound from narrative descriptions.
Concept visualization — Quickly visualize ideas, moods, or environments.
Storyboard prototyping — Turn scene descriptions into video drafts before full production.
Marketing assets — Produce short-form video content directly from copy briefs.
Educational content — Illustrate concepts, processes, or historical scenes through natural language.

Pricing

Billing is based on the duration of the generated video, charged at a flat per-second rate.

SKU	Rate
Per second of output	$0.125

Formula: max(3, duration) × $0.125

Billing is per second, with a 3-second minimum — durations below 3s are billed as 3s.
Example: a 10-second video costs 10 × $0.125 = $1.25.
Example: a 3-second video costs 3 × $0.125 = $0.375.

Gemini Omni Flash Text-to-Video API by Google

Inmatning

Utmatning

Parametrar

Kodexempel

Installera

Autentisering

HTTP-huvuden

Skicka en förfrågan

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Svar

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Polling-exempel

Statusvärden

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Uppladdningsexempel

Svar

Input Schema

Exempel på förfrågningsinnehåll

Output Schema

Exempelsvar

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Funktioner

MCP Server

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Konfiguration

Tillgängliga verktyg

API Schema

LLM-vänlig Promptmall

Gemini Omni Flash — Text to Video

Overview

Inputs

Key Capabilities

Input Parameters

Use Cases

Pricing

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Inmatning

Utmatning

Parametrar

Kodexempel

Installera

Autentisering

HTTP-huvuden

Skicka en förfrågan

Skicka en förfrågan

Förfrågningsinnehåll

Svar

Kontrollera status

Polling-exempel

Statusvärden

Slutfört svar

Ladda upp filer

Uppladdningsexempel

Svar

Input Schema