Can HappyHorse 1.1 generate videos from text prompts?

Yes. HappyHorse 1.1 supports text-to-video generation and can create 720p or 1080p videos from detailed text prompts.

What improvements does HappyHorse 1.1 offer over HappyHorse 1.0?

HappyHorse 1.1 improves prompt understanding, motion smoothness, temporal consistency, and overall video quality across T2V, I2V, and R2V workflows.

Does HappyHorse 1.1 support image-to-video generation?

Yes. Users can upload an image and animate it into a short video sequence with optional prompt guidance.

Can HappyHorse 1.1 create videos with synchronized audio?

The HappyHorse family is built around a unified audio-video architecture that generates visual and audio elements together, improving synchronization and lip-sync quality.

How does HappyHorse 1.1 compare with Seedance 2.5?

HappyHorse 1.1 focuses on high-quality short-form video generation and audiovisual synchronization, while Seedance 2.5 emphasizes longer videos and large-scale multimodal reference control.

Is HappyHorse 1.1 suitable for commercial content creation?

Yes. It is designed for advertising, marketing, social media production, storytelling, and other professional video-generation use cases.

What video resolutions does HappyHorse 1.1 support?

HappyHorse 1.1 supports both 720p and 1080p output modes.

Does HappyHorse 1.1 support reference-based video generation?

Yes. The model includes reference-to-video workflows that allow creators to use existing visual assets to guide generation.

Affordable Happy Horse 1.1 API | text-to-video

Technical Specifications of HappyHorse 1.1

Item	HappyHorse 1.1
Provider	Alibaba
Model Type	AI Video Generation
Generation Modes	Text-to-Video (T2V), Image-to-Video (I2V), Reference-to-Video (R2V)
Input Types	Text, Images, Reference Media
Output Types	Video
Resolution	720p and 1080p supported
Video Duration	3–15 seconds
Aspect Ratios	16:9, 9:16, 1:1, 4:3, 3:4, 4:5, 5:4, 9:21, 21:9
Prompt Length	Up to 5,000 non-Chinese characters
Commercial Usage	Supported through API partners
Primary Focus	Cinematic video generation with synchronized audio and motion consistency

HappyHorse 1.1 is the successor to HappyHorse 1.0, Alibaba's high-performing AI video model. The 1.1 release improves prompt adherence, motion quality, temporal consistency, and reference-based generation workflows while maintaining support for text-to-video, image-to-video, and reference-to-video generation.

What is HappyHorse 1.1?

HappyHorse 1.1 is a multimodal video-generation model designed for professional content creation, advertising, short films, social media production, and storytelling. It extends the capabilities of HappyHorse 1.0—which gained significant attention after ranking highly in independent video-generation evaluations—with stronger scene coherence and improved visual fidelity.

Unlike many video models that generate silent video first and add audio later, the HappyHorse architecture is known for jointly modeling video and audio generation, helping maintain lip-sync accuracy and audiovisual consistency.

Core Capabilities of Happy Horse 1.1 API

Text-to-Video Generation

Happy Horse 1.1 can convert detailed prompts into short AI-generated videos with stronger semantic understanding and more accurate prompt adherence. It supports multilingual prompts and is suitable for cinematic scenes, product visuals, social media clips, storytelling videos, and creative concept generation.

Image-to-Video Generation

With image-to-video support, Happy Horse 1.1 can animate a static image while preserving key visual details such as character identity, product appearance, background composition, and artistic style. This makes it useful for ecommerce videos, character animation, app demos, social media ads, and branded visual content.

Reference-to-Video Generation

Happy Horse 1.1 supports reference-based video generation using multiple images. According to Kie.ai’s HappyHorse-1.1 page, reference-to-video workflows can use up to 9 reference images, helping developers guide subject appearance, scene composition, style, and creative direction more precisely.

Flexible Video Controls

Happy Horse 1.1 supports common production controls including 720p and 1080p resolution options, multiple aspect ratios such as 16:9, 9:16, 1:1, 4:3, 3:4, 4:5, 5:4, 9:21, and 21:9, and video durations from 3 to 15 seconds. These options make it suitable for both horizontal video, vertical short-form content, square social media posts, and ultra-wide cinematic previews.

Stronger Motion and Consistency

Happy Horse 1.1 focuses on smoother movement, more realistic character actions, improved camera transitions, and stronger identity preservation. For workflows that require the same character, product, or brand asset to remain visually stable across generations, this consistency is one of the model’s main advantages.

Happy Horse 1.1 API vs Seedance 2.0 API

Feature	Happy Horse 1.1 API	Seedance 2.0 API
Best for	Character consistency, reference-to-video, brand-controlled video generation	Cinematic text-to-video, multimodal video generation, audio-video workflows
Main workflows	Text-to-video, image-to-video, reference-to-video	Text, image, video, and audio-based generation depending on integration
Reference support	Up to 9 reference images on Kie.ai	CometAPI’s Seedance 2.0 guide describes broader multimodal references, including images, video, and audio
Output duration	3-15 seconds	Commonly 4-15 seconds
Resolution	720p and 1080p	Varies by tier; CometAPI materials mention 480p/720p and up to 2K in some tiers
Strength	Preserving characters, style, objects, and reference-image intent	Director-style motion, native audio-video generation, cinematic prompt execution
Recommended use	Product demos, virtual characters, brand videos, story scenes, consistent visual assets	Marketing videos, cinematic shorts, social media clips, audio-synced content

Choose Happy Horse 1.1 API when your workflow depends on visual consistency, reference image control, character preservation, or brand-safe repeatability. Choose Seedance 2.0 API when you need stronger audio-video generation, cinematic prompt interpretation, or more complex multimodal direction.

How to Build with HappyHorse 1.1 API on CometAPI

Step 1: Get Your API Key

Create a CometAPI account, generate an API key, and verify the current HappyHorse 1.1 model(happyhorse-1.1) identifier available through the platform and check HappyHorse API doc.

Step 2: Prompt

Provide text prompts, upload images, or add reference images to guide the HappyHorse-1.1 video generation process.

Step 3: Generate video

Create a HappyHorse text-to-video task through CometAPI with POST /v1/videos, then poll the task and download the completed MP4 file. If the task is still running, the API returns a JSON error instead of video bytes.

What Can You Build with Happy Horse 1.1 API on CometAPI?

AI Video Generator Apps

Build a full AI video creation platform that supports text-to-video, image-to-video, and reference-to-video generation through one API layer.

Product Demo Videos

Turn product photos into short motion clips for ecommerce, SaaS launches, mobile apps, hardware showcases, and digital advertising.

Character-Consistent Storytelling

Create narrative videos, virtual influencers, animated brand characters, and social media story clips while keeping character identity and visual style consistent.

Brand and Marketing Content

Use reference images to preserve brand assets, colors, product design, mascot appearance, campaign style, and scene direction across multiple video outputs.

Creative Prototyping

Generate concept videos, mood boards, storyboard previews, pitch materials, and short-form campaign drafts before investing in full production.

Why Choose Happy Horse 1.1 API on CometAPI?

One API for 500+ Models

CometAPI provides unified access to 500+ AI models, including text, image, audio, and video generation models. Developers can integrate once and switch models without rebuilding their application.

Lower Integration Overhead

Instead of managing separate accounts, credentials, billing systems, and API formats for every model provider, CometAPI gives teams a single API key and unified access layer.

Easier Model Comparison

Happy Horse 1.1 can be tested alongside other video models such as Seedance, Kling, Veo, and Sora-style models. This helps teams compare quality, cost, latency, and prompt performance before choosing a production model.

Built for Production Video Workflows

CometAPI supports async video generation patterns, task polling, usage tracking, and model routing, making it easier to move from prototype to production.

Pricing for Happy Horse 1.1

Explore competitive pricing for Happy Horse 1.1, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how Happy Horse 1.1 can enhance your projects while keeping costs manageable.

Happyhorse Video Generation Pricing

Pricing (Per Second)

Model	720p	1080p
`happyhorse-1.1`	$0.112	$0.144

💡 Billed per second. Total cost = price per second × video duration (seconds).

Sample code and API for Happy Horse 1.1

Access comprehensive sample code and API resources for Happy Horse 1.1 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of Happy Horse 1.1 in your projects.

Python
JavaScript
Curl

# Create a video with happyhorse-1.1 using raw HTTP requests
import os
import time
import requests

api_key = os.environ.get("COMETAPI_KEY")
base_url = "https://api.cometapi.com/v1"
headers = {"Authorization": f"Bearer {api_key}"}
prompt = "A close-up cinematic shot of a ceramic coffee cup on a wooden table. Steam rises slowly while morning light moves across the background."

# Step 1: Submit the video generation request
print("Submitting video generation request...")
response = requests.post(
    f"{base_url}/videos",
    headers=headers,
    files={
        "model": (None, "happyhorse-1.1"),
        "prompt": (None, prompt),
        "seconds": (None, "3"),
        "size": (None, "1920x1080"),
    },
)

result = response.json()
print(f"Response: {result}")

video_id = result.get("id") or result.get("task_id")
print(f"Video ID: {video_id}")

# Step 2: Poll for progress until 100%
print("
Checking video generation progress...")
while True:
    try:
        status_response = requests.get(f"{base_url}/videos/{video_id}", headers=headers)
        status_result = status_response.json()

        data = status_result.get("data") or status_result
        progress = data.get("progress", "0%")
        status = data.get("status", "unknown")

        print(f"Progress: {progress}, Status: {status}")

        if status in ["FAILURE", "failed", "error"]:
            print("Video generation failed!")
            print(status_result)
            exit(1)

        if progress == "100%" or progress == 100 or status in ["completed", "success"]:
            print("Video generation completed!")
            break
    except Exception as e:
        print(f"Temporary error: {e}, retrying...")

    time.sleep(10)

# Step 3: Download the video to output directory
print(f"
Downloading video to ./output/{video_id}.mp4...")
os.makedirs("./output", exist_ok=True)

video_response = requests.get(f"{base_url}/videos/{video_id}/content", headers=headers)

output_path = f"./output/{video_id}.mp4"
with open(output_path, "wb") as f:
    f.write(video_response.content)

if os.path.exists(output_path):
    file_size = os.path.getsize(output_path)
    print(f"Video saved to {output_path}")
    print(f"File size: {file_size} bytes")
else:
    print("Failed to download video")
    exit(1)

Python Code Example

# Create a video with happyhorse-1.1 using raw HTTP requests
import os
import time
import requests

api_key = os.environ.get("COMETAPI_KEY")
base_url = "https://api.cometapi.com/v1"
headers = {"Authorization": f"Bearer {api_key}"}
prompt = "A close-up cinematic shot of a ceramic coffee cup on a wooden table. Steam rises slowly while morning light moves across the background."

# Step 1: Submit the video generation request
print("Submitting video generation request...")
response = requests.post(
    f"{base_url}/videos",
    headers=headers,
    files={
        "model": (None, "happyhorse-1.1"),
        "prompt": (None, prompt),
        "seconds": (None, "3"),
        "size": (None, "1920x1080"),
    },
)

result = response.json()
print(f"Response: {result}")

video_id = result.get("id") or result.get("task_id")
print(f"Video ID: {video_id}")

# Step 2: Poll for progress until 100%
print("\nChecking video generation progress...")
while True:
    try:
        status_response = requests.get(f"{base_url}/videos/{video_id}", headers=headers)
        status_result = status_response.json()

        data = status_result.get("data") or status_result
        progress = data.get("progress", "0%")
        status = data.get("status", "unknown")

        print(f"Progress: {progress}, Status: {status}")

        if status in ["FAILURE", "failed", "error"]:
            print("Video generation failed!")
            print(status_result)
            exit(1)

        if progress == "100%" or progress == 100 or status in ["completed", "success"]:
            print("Video generation completed!")
            break
    except Exception as e:
        print(f"Temporary error: {e}, retrying...")

    time.sleep(10)

# Step 3: Download the video to output directory
print(f"\nDownloading video to ./output/{video_id}.mp4...")
os.makedirs("./output", exist_ok=True)

video_response = requests.get(f"{base_url}/videos/{video_id}/content", headers=headers)

output_path = f"./output/{video_id}.mp4"
with open(output_path, "wb") as f:
    f.write(video_response.content)

if os.path.exists(output_path):
    file_size = os.path.getsize(output_path)
    print(f"Video saved to {output_path}")
    print(f"File size: {file_size} bytes")
else:
    print("Failed to download video")
    exit(1)

JavaScript Code Example

// Create a video with happyhorse-1.1 using raw HTTP requests
import fs from "fs";
import path from "path";

const apiKey = process.env.COMETAPI_KEY;
const baseUrl = "https://api.cometapi.com/v1";
const headers = { Authorization: `Bearer ${apiKey}` };
const prompt = "A close-up cinematic shot of a ceramic coffee cup on a wooden table. Steam rises slowly while morning light moves across the background.";

function sleep(ms) {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

// Step 1: Submit the video generation request
console.log("Submitting video generation request...");
const formData = new FormData();
formData.append("model", "happyhorse-1.1");
formData.append("prompt", prompt);
formData.append("seconds", "3");
formData.append("size", "1920x1080");

const submitResponse = await fetch(`${baseUrl}/videos`, {
  method: "POST",
  headers,
  body: formData,
});

const result = await submitResponse.json();
console.log("Response:", JSON.stringify(result, null, 2));

const videoId = result.id || result.task_id;
console.log("Video ID:", videoId);

// Step 2: Poll for progress until 100%
console.log("\nChecking video generation progress...");
while (true) {
  try {
    const statusResponse = await fetch(`${baseUrl}/videos/${videoId}`, { headers });
    const statusResult = await statusResponse.json();
    const data = statusResult.data || statusResult;
    const progress = data.progress || "0%";
    const status = data.status || "unknown";

    console.log(`Progress: ${progress}, Status: ${status}`);

    if (status === "FAILURE" || status === "failed" || status === "error") {
      console.log("Video generation failed!");
      console.log(JSON.stringify(statusResult, null, 2));
      process.exit(1);
    }

    if (progress === "100%" || progress === 100 || status === "completed" || status === "success") {
      console.log("Video generation completed!");
      break;
    }
  } catch (e) {
    console.log(`Temporary error: ${e.message}, retrying...`);
  }

  await sleep(10000);
}

// Step 3: Download the video to output directory
console.log(`\nDownloading video to ./output/${videoId}.mp4...`);
fs.mkdirSync("./output", { recursive: true });

const videoResponse = await fetch(`${baseUrl}/videos/${videoId}/content`, { headers });
const outputPath = path.join("./output", `${videoId}.mp4`);
fs.writeFileSync(outputPath, Buffer.from(await videoResponse.arrayBuffer()));

if (fs.existsSync(outputPath)) {
  const stats = fs.statSync(outputPath);
  console.log(`Video saved to ${outputPath}`);
  console.log(`File size: ${stats.size} bytes`);
} else {
  console.log("Failed to download video");
  process.exit(1);
}

Curl Code Example

# Create a video with happyhorse-1.1
# Step 1: Submit the video generation request
echo "Submitting video generation request..."
response=$(curl -s https://api.cometapi.com/v1/videos \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -F "model=happyhorse-1.1" \
  -F "prompt=A close-up cinematic shot of a ceramic coffee cup on a wooden table. Steam rises slowly while morning light moves across the background." \
  -F "seconds=3" \
  -F "size=1920x1080")

echo "Response: $response"

# Extract video_id from response (handle JSON with spaces like "id": "xxx")
video_id=$(echo "$response" | tr -d '\n' | sed 's/.*"id"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/')
echo "Video ID: $video_id"

# Step 2: Poll for progress until 100%
echo ""
echo "Checking video generation progress..."
while true; do
  status_response=$(curl -s "https://api.cometapi.com/v1/videos/$video_id" \
    -H "Authorization: Bearer $COMETAPI_KEY")

  progress=$(echo "$status_response" | grep -o '"progress"[[:space:]]*:[[:space:]]*"\?[^",}]*"\?' | head -1 | sed 's/.*:[[:space:]]*"\?//;s/"$//')
  status=$(echo "$status_response" | grep -o '"status"[[:space:]]*:[[:space:]]*"[^"]*"' | head -1 | sed 's/.*"status"[[:space:]]*:[[:space:]]*"//;s/"$//')

  echo "Progress: $progress, Status: $status"

  if [ "$status" = "FAILURE" ] || [ "$status" = "failed" ] || [ "$status" = "error" ]; then
    echo "Video generation failed!"
    exit 1
  fi

  if [ "$progress" = "100%" ] || [ "$progress" = "100" ] || [ "$status" = "completed" ] || [ "$status" = "success" ]; then
    echo "Video generation completed!"
    break
  fi

  sleep 10
done

# Step 3: Download the video to output directory
echo ""
echo "Downloading video to ./output/$video_id.mp4..."
mkdir -p ./output
curl -s "https://api.cometapi.com/v1/videos/$video_id/content" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -o "./output/$video_id.mp4"

if [ -f "./output/$video_id.mp4" ]; then
  echo "Video saved to ./output/$video_id.mp4"
  ls -la "./output/$video_id.mp4"
else
  echo "Failed to download video"
  exit 1
fi

Versions of Happy Horse 1.1

The reason Happy Horse 1.1 has multiple snapshots may include potential factors such as variations in output after updates requiring older snapshots for consistency, providing developers a transition period for adaptation and migration, and different snapshots corresponding to global or regional endpoints to optimize user experience. For detailed differences between versions, please refer to the official documentation.

version
happyhorse-1.1