Vidu Q3 Reference-to-Video API by Vidu
Vidu Q3 Reference-to-Video generates videos from 1-4 reference images with consistent subjects. Features intelligent camera switching with better consistency across multiple camera positions, audio support, and resolutions up to 1080p.
INPUT
OUTPUT
IdleYour request will cost $0.042 per run. For $10 you can run this model approximately 238 times.
Here's what you can do next:
Code Example
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": "vidu/q3/reference-to-video",
"prompt": "A beautiful sunset over the ocean with gentle waves",
"width": 512,
"height": 512,
"duration": 3,
"fps": 24,
}
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()Install
Install the required package for your language.
pip install requestsAuthentication
All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Headers
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.
Submit a request
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())Submit a Request
Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.
/api/v1/model/generateVideoRequest Body
import requests
url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "vidu/q3/reference-to-video",
"input": {
"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['id']}")
print(f"Status: {result['status']}")Response
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Check Status
Poll the prediction endpoint to check the current status of your request.
/api/v1/model/prediction/{prediction_id}Polling Example
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)Status Values
processingThe request is still being processed.completedGeneration is complete. Outputs are available.succeededGeneration succeeded. Outputs are available.failedGeneration failed. Check the error field.Completed Response
{
"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"
}
}Upload Files
Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.
/api/v1/model/uploadMediaUpload Example
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}")Response
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
The following parameters are accepted in the request body.
No parameters available.
Example Request Body
{
"model": "vidu/q3/reference-to-video"
}Output Schema
The API returns a prediction response with the generated output URLs.
Example Response
{
"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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.
Supported Clients
Install
npx skills add AtlasCloudAI/atlas-cloud-skillsSetup API Key
Get your API key from the Atlas Cloud dashboard and set it as an environment variable.
export ATLASCLOUD_API_KEY="your-api-key-here"Capabilities
Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.
MCP Server
Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.
Supported Clients
Install
npx -y atlascloud-mcpConfiguration
Add the following configuration to your IDE's MCP settings file.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
API Schema
Schema not availableNo examples available
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Log In1. Introduction
Vidu Q3 is an advanced AI video generation model developed by Shengshu Technology (生数科技) in collaboration with Tsinghua University. Released on January 30, 2026, Vidu Q3 is designed to produce high-fidelity, synchronized audio-visual content with industry-leading continuous video length and native support for integrated audio generation.
The model represents a significant advancement in automated video synthesis by unifying multiple complex video generation tasks—such as lip-synced dialogue, dynamic camera movements, and multi-shot storytelling—into a single-pass framework. Leveraging a novel Transformer-based diffusion architecture, Vidu Q3 sets a new standard for cinematic and marketing video content creation with its combination of spatial-temporal coherence, multimodal input flexibility, and real-time directorial control.
2. Key Features & Innovations
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Native Audio-Video Synchronization: Vidu Q3 generates lip-synced dialogue, sound effects, and background music simultaneously within a single pass, ensuring precise temporal alignment between audio tracks and visual lip movements without requiring post-processing.
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Extended High-Definition Video Generation: Supports up to 16 seconds of continuous video at 1080p resolution and 24 frames per second—the longest continuous generation duration among leading competitors—enabling more complex storytelling sequences.
-
Smart Cuts for Scene Detection: Integrates automatic scene boundary detection and multi-shot narrative transitions, which facilitate the smooth generation of dynamic video scenes without manual intervention.
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Native Camera Control: Allows frame-level directorial commands such as pans, push-ins, and tracking shots within the generation pipeline, granting users granular cinematic control over the resulting video composition.
-
Multimodal Input Flexibility: Accepts both text-to-video and image-to-video inputs with configurable start and end frame controls, enabling versatile use cases that range from scripted storyboarding to visual style transfer.
-
Transformer-based Diffusion Architecture with Spatiotemporal Attention: The underlying Universal Vision Transformer (U-ViT) utilizes spatiotemporal attention mechanisms instead of conventional convolutional U-Nets, improving motion consistency and temporal coherence across generated frames.
-
Model Variants Tailored for Fidelity and Speed: Offers differentiated configurations including Q3 Pro for maximum visual fidelity, Q3 Turbo optimized for higher generation speed, and the legacy Q2 Series focused on character consistency.
3. Model Architecture & Technical Details
Vidu Q3 is architected on the U-ViT (Universal Vision Transformer) framework, replacing traditional convolutional U-Net diffusion models with a Transformer-based diffusion approach. This design enables enhanced modeling of spatiotemporal dependencies essential for consistent video generation with coherent motion and scene dynamics.
The training utilized large-scale, multimodal datasets encompassing paired video, audio, and textual data to foster robust cross-modal understanding and synthesis. Multiple training stages refined resolution and temporal granularity, progressing toward 1080p, 24fps output over sequences up to 16 seconds.
Specialized modules incorporated include spatiotemporal attention layers for motion consistency and native audio-visual synchronization, alongside smart cut detection layers for automatic scene segmentation. The pipeline supports multimodal conditioning inputs (text and images) with frame-level temporal control allowing start and end frame specification.
Post-training refinement employed techniques such as supervised fine-tuning on domain-specific cinematic data and continuous evaluation on video generation benchmarks to optimize lip-sync accuracy and camera control responsiveness.
4. Performance Highlights
Vidu Q3 demonstrably leads in multiple benchmark categories, particularly for continuous video length and audiovisual integration quality. It achieves an ELO rating between approximately 1220–1244 on the Artificial Analysis Video Arena, outperforming contemporaries such as Runway Gen-4.5 and Kling 2.5 Turbo.
| Rank | Model | Developer | ELO Score | Release Date |
|---|---|---|---|---|
| 1 | Sora 2 | [Undisclosed] | ~1250+ | Pre-2026 |
| 2 | Vidu Q3 | Shengshu Tech & Tsinghua | 1220–1244 | Jan 30, 2026 |
| 3 | Runway Gen-4.5 | Runway | ~1200 | 2025 |
| 4 | Kling 2.5 Turbo | Kling AI | ~1190 | Late 2025 |
Qualitatively, Vidu Q3 delivers superior cinematics including advanced native camera motion and scene transitions compared to Veo 3.1 and Grok Imagine, while maintaining better audio integration than Sora 2 and Kling 3.0. Its 16-second generation duration notably surpasses the typical 8-15 second range of competitors, allowing more complex narratives per generation.
5. Intended Use & Applications
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Commercial Advertising: Produces 12-16 second product demonstration videos with synchronized audio and high realism, suitable for digital marketing campaigns.
-
Marketing Videos: Generates videos combining dialogue, sound effects, and background music tailored for brand storytelling and promotional content.
-
Cinematic Short-Form Storytelling: Enables filmmakers and content creators to automatically craft multi-shot video sequences with directorial camera control and scene transitions.
-
Social Media Content Creation: Facilitates rapid production of engaging social videos with lip-synced speech and dynamic visuals optimized for platform consumption.
-
Architectural Visualization: Visualizes architectural designs with realistic camera movements and synchronized ambient sounds enhancing presentation fidelity.
-
Educational Video Production: Supports creation of instructional content blending narrated audio with synchronized visual demonstrations and scene changes.
