Veo3.1 Reference-to-Video API by Google
google/veo3.1/reference-to-video
Reference-to-video
Create richly detailed videos guided by visual references. Veo 3.1 Reference-to-Video preserves characters, style, and composition across scenes for consistent, visually coherent storytelling.
INPUT
Loading parameter configuration...
OUTPUT
IdleYour generated videos will appear here
Configure your settings and click Run to get started
Your request will cost $0.2 per run. For $10 you can run this model approximately 50 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": "google/veo3.1/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.
bash
pip install requestsAuthentication
All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.
bash
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Headers
python
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Keep your API key secure
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.
POST
/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": "google/veo3.1/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.
GET
/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.
POST
/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.
Total: 0Required: 0Optional: 0
No parameters available.
Example Request Body
json
{
"model": "google/veo3.1/reference-to-video"
}Output Schema
The API returns a prediction response with the generated output URLs.
idstringrequired
Unique identifier for the prediction.
statusstringrequired
Current status of the prediction.
processingcompletedsucceededfailed
modelstringrequired
The model used for generation.
outputsarray[string]
Array of output URLs. Available when status is "completed".
errorstring
Error message if status is "failed".
metricsobject
Performance metrics.
predict_timenumber
Time taken for video generation in seconds.
created_atstringrequired
ISO 8601 timestamp when the prediction was created.
completed_atstring
ISO 8601 timestamp when the prediction was completed.
Example Response
json
{
"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
Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ supported clients
Install
bash
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.
bash
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.
Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.
Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.
LLM ChatChat with Qwen, DeepSeek, and other large language models.
Media UploadUpload local files for image editing and image-to-video workflows.
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
Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ supported clients
Install
bash
npx -y atlascloud-mcpConfiguration
Add the following configuration to your IDE's MCP settings file.
json
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
atlas_generate_imageGenerate images from text prompts.
atlas_generate_videoCreate videos from text or images.
atlas_chatChat with large language models.
atlas_list_modelsBrowse 300+ available AI models.
atlas_quick_generateOne-step content creation with auto model selection.
atlas_upload_mediaUpload local files for API workflows.
Learn more
API Schema
Schema not availableNo examples available
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Log InGoogle Veo 3.1 — Reference-to-Video Model
Veo 3.1 Reference-to-Video brings static images to life by combining visual reference consistency with cinematic motion generation. Powered by Google DeepMind’s next-generation Veo 3.1 architecture, this model transforms up to three reference images into coherent 5-second videos with smooth motion, accurate visual alignment, and synchronized native audio.
🌟 Key Features
🧠 Multi-Image Reference Support
- Accepts up to three reference images to define the subject, environment, or style.
- Maintains consistent identity, lighting, and appearance across frames.
- Ideal for animating people, objects, or scenes with reliable fidelity.
🎬 Cinematic Video Generation
- Produces 5-second motion clips at 1080p or 720p resolution.
- Adds camera dynamics such as panning, zooming, or subtle perspective drift.
- Supports synchronized audio generation, matching dialogue or ambient context.
💡 Smart Prompt Adherence
- Interprets both text instructions and visual cues for precise motion storytelling.
- Automatically harmonizes character interactions, props, and backgrounds.
⚙️ Capabilities
-
Input:
- Up to 3 reference images (JPEG / PNG / WEBP)
- Text prompt describing motion, action, and scene context
-
Output:
- 8-second MP4 video (720p or 1080p)
- Optional synchronized audio
-
Negative Prompt (optional):
- Exclude unwanted artifacts or elements (e.g., “no text”, “no flicker”).
-
Seed (optional):
- Reproduce specific results for consistent creative control.
💰 Pricing
| Duration | Resolution | With Audio | Without Audio |
|---|---|---|---|
| 8 seconds | 720p | $3.20 | $1.60 |
| 8 seconds | 1080p | $3.20 | $1.60 |
✅ Commercial use allowed
🧩 How to Use
- Upload up to 3 reference images — define the subject, object, or visual style.
- Write a text prompt — describe the action, setting, and camera motion.
- (Optional) Add a negative prompt to remove unwanted details.
- Choose resolution (720p or 1080p).
- (Optional) Enable audio generation for synchronized sound.
- Click Run to generate your 5-second cinematic video.
💡 Best Practices
- Use clear, well-lit reference images with similar styles and proportions.
- Keep prompts concise but specific (e.g., “The man in image 1 waves to the penguins in image 2 under bright sunlight”).
- Avoid overly complex scenarios with many characters or fast movement.
- Enable audio for more immersive storytelling results.
📝 Notes
- Ensure uploaded images are valid and accessible URLs or uploaded locally.
- If the output looks unstable, reduce reference count or simplify the prompt.
- Follow Google’s content safety rules; modify the prompt if flagged.
- For best performance, prefer portrait-oriented subjects and balanced lighting.
