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Video Generation

ShuYou supports calling video generation models via the Vertex AI protocol. This guide explains how to generate videos with ShuYou.
Video generation models can automatically produce high-quality video content from text descriptions. ShuYou aggregates leading video generation models such as Google Veo and ByteDance Seedance, allowing you to call them easily through a unified API interface.

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API reference

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Supported Models

The currently supported video generation models in the API reference include (continuously updated):
  • doubao-seedance-2.0 — ByteDance Seedance 2.0
  • doubao-seedance-2.0-fast — ByteDance Seedance 2.0 Fast
  • happyhorse-1.0-t2v — HappyHorse 1.0 T2V
  • happyhorse-1.0-r2v — HappyHorse 1.0 R2V
  • happyhorse-1.0-i2v — HappyHorse 1.0 I2V
  • happyhorse-1.0-video-edit — HappyHorse 1.0 Video Edit
Visit the ShuYou model list to search and view all available video generation models.

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Text-to-Video

Generate videos directly from a text prompt using an asynchronous workflow: submit a generation request first, then poll until generation completes, and finally retrieve the result.
Google Veo 3.1
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Step 1: Submit a video generation request
operation = client.models.generate_videos(
    model="google/veo-3.1-generate-001",  
    prompt="A golden retriever running on the beach at sunset"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)
Seedance 1.5 Pro
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Step 1: Submit a video generation request
operation = client.models.generate_videos(
    model="volcengine/doubao-seedance-1.5-pro",  
    prompt="A golden retriever running on the beach at sunset"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)
Seedance 2
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Step 1: Submit a video generation request
operation = client.models.generate_videos(
    model="volcengine/doubao-seedance-2",  
    prompt="A golden retriever running on the beach at sunset"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)

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Image-to-Video

In addition to text-to-video, ShuYou also supports passing an image as the starting frame and generating a video using a text prompt. Provide the image data via the image parameter.
Google Veo 3.1
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Read a local image
with open("input_image.png", "rb") as f:
    image_bytes = f.read()

# Step 1: Submit an image-to-video request
operation = client.models.generate_videos(
    model="google/veo-3.1-generate-001",  
    image=types.Image(image_bytes=image_bytes, mime_type="image/png"),  
    prompt="The dog stands up and runs toward the ocean waves"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)
Seedance 1.5 Pro
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Read a local image
with open("input_image.png", "rb") as f:
    image_bytes = f.read()

# Step 1: Submit an image-to-video request
operation = client.models.generate_videos(
    model="volcengine/doubao-seedance-1.5-pro",  
    image=types.Image(image_bytes=image_bytes, mime_type="image/png"),  
    prompt="The dog stands up and runs toward the ocean waves"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)
Seedance 2
from google import genai
from google.genai import types
import time

client = genai.Client(
    api_key="$ShuYou_API_KEY",  # Replace with your API Key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version="v1",
        base_url="https://api.shuyou.ai"
    )
)

# Read a local image
with open("input_image.png", "rb") as f:
    image_bytes = f.read()

# Step 1: Submit an image-to-video request
operation = client.models.generate_videos(
    model="volcengine/doubao-seedance-2",  
    image=types.Image(image_bytes=image_bytes, mime_type="image/png"),  
    prompt="The dog stands up and runs toward the ocean waves"
)

# Step 2: Poll until generation is complete
while not operation.done:
    time.sleep(15)
    operation = client.operations.get(operation)

# Step 3: Retrieve the generated results
for video in operation.response.generated_videos:
    print(video)
  • The image parameter is passed via types.Image, which supports image_bytes (binary data) and mime_type (e.g., image/png, image/jpeg).
  • The prompt parameter is optional and is used to describe how the content in the image should move or change.
  • The image will be used as the starting frame of the video, and the model will generate subsequent animation based on the image content and the prompt.

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Configuration

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Required Parameters

  • api_key: Your ShuYou API key
  • vertexai: Must be set to true to enable the Vertex AI protocol
  • base_url: ShuYou Vertex AI endpoint https://api.shuyou.ai
  • model: The video generation model name, such as google/veo-3.1-generate-001
  • prompt: A text prompt describing the video content to generate

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Optional Parameters

You can customize video properties such as aspect ratio, duration, and audio via the config parameter:
ParameterTypeDescriptionExample Values
aspectRatiostrVideo aspect ratio"16:9", "9:16", "1:1"
resolutionstrVideo resolution"720p", "1080p"
durationSecondsintVideo duration (seconds)5, 8, 10
generateAudioboolWhether to generate audioTrue, False
Configuration example: 
operation = client.models.generate_videos(
    model="google/veo-3.1-generate-001",
    prompt="A cat playing piano in a cozy room with warm lighting",
    config=types.GenerateVideosConfig(
        aspectRatio="16:9",       # Landscape 16:9  #
        resolution="720p",        # 720p resolution  #
        durationSeconds=8,        # 8-second video duration  #
        generateAudio=True,       # Generate a video with audio  #
    )
)
Support for optional parameters may vary by model. If you pass a parameter value that the model does not support, the API will return an error. We recommend testing with default parameters first, then adjusting gradually.

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Call Flow

Video generation is an asynchronous process with three steps:
  1. Submit request (generate_videos): Send a video generation request and receive an operation object
  2. Poll status (operations.get): Check generation status periodically; a 15-second interval is recommended
  3. Retrieve results: When operation.done is True, get the videos from operation.response.generated_videos
Video generation typically takes a while (from tens of seconds to several minutes). Please be patient while polling completes, and avoid setting the polling interval too short.

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Best Practices

  1. Prompt optimization: Use clear, specific scene descriptions, including subject, actions, environment, lighting, and other key elements
  2. Polling interval: Use a 15-second polling interval to avoid overly frequent requests
  3. Error handling: Add exception handling and a timeout mechanism to prevent infinite polling
  4. Model selection: Choose the right model for your needs — Veo 3.1 excels at high-quality general video generation, while the Seedance family has unique strengths in specific scenarios