pixverse/c1/reference-to-video

reference-to-video

Pixverse c1 Reference-to-Video API by PIXVERSE

pixverse/c1/reference-to-video

Reference-to-video

Pixverse c1 Reference-to-Video model. High-quality video generation from image prompts.

Pixverse c1 Reference-to-Video is developed by PixVerse. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Images *

MIN: 1 / MAX: 7

Duration

Aspect Ratio

Quality

Sound

Seed

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.03 per run. For $10 you can run this model approximately 333 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

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": "pixverse/c1/reference-to-video",  # Required. model name
    "duration": 5,  # The duration of the generated media in seconds (1-15)
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The positive prompt for the generation
    "sound": True,  # Whether sound is generated simultaneously when generating a video
    "quality": "720p",  # The resolution of the generated video. options: 360p | 540p | 720p | 1080p
    "aspect_ratio": "16:9",  # The aspect ratio of the generated video
    "seed": 1,  # Random seed for reproducibility
    "images": [
        "https://example.com/image1.jpg"
    ],  # Required. Image reference array (1–7 items)
}

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 requests

Authentication

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}"
}

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/generateVideo

Request Body

import requests

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

data = {
    "model": "pixverse/c1/reference-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']}")

Response

{
  "code": 200,
  "data": {
    "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/uploadMedia

Upload 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: 8Required: 3Optional: 5

modelstringrequired

model name

Default: "pixverse/c1/reference-to-video"

durationinteger

The duration of the generated media in seconds (1-15).

Default: 5

123456789101112131415

promptstringrequired

The positive prompt for the generation.

soundboolean

Whether sound is generated simultaneously when generating a video.

Default: true

qualitystring

The resolution of the generated video.

Default: "720p"

360p540p720p1080p

aspect_ratiostring

The aspect ratio of the generated video.

Default: "16:9"

16:99:161:14:33:42:33:221:9

seedinteger

Random seed for reproducibility.

imagesarray[object]required

Image reference array (1–7 items).

Min items: 1Max items: 7

imagestringrequired

First frame of the video; Notes: 1.Public URL or Base64. 2.Aspect ratios must be close: ratio between start/end frame must be in 0.8~1.25. 3.Format: png, jpeg, jpg, webp. 4.The post body of the HTTP request should not exceed 20MB

typestring

Reference type. For v5.5+: "subject" or "background".

subjectbackground

ref_namestring

Optional name for the reference image (v5.5+). Used to improve accuracy. Max 30 bytes (UTF-8).

Example Request Body

{
  "model": "pixverse/c1/reference-to-video",
  "duration": 5,
  "prompt": "A beautiful landscape",
  "sound": true,
  "quality": "720p",
  "aspect_ratio": "16:9",
  "images": [
    {
      "image": "example_image",
      "type": "example_type",
      "ref_name": "example_ref_name"
    }
  ]
}

Output Schema

The API returns a prediction response with the generated output URLs.

created_atstring

ISO timestamp of when the request was created.

idstring

Unique identifier for the prediction.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content.

statusstring

Status of the task: created, processing, completed, or failed.

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 400+ 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

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup 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.

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.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ 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

npx -y atlascloud-mcp

Configuration

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

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "pixverse/c1/reference-to-video"
          },
          "duration": {
            "default": 5,
            "description": "The duration of the generated media in seconds (1-15).",
            "enum": [
              1,
              2,
              3,
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15
            ],
            "type": "integer",
            "x-ui-component": "select"
          },
          "prompt": {
            "description": "The positive prompt for the generation.",
            "type": "string",
            "maxLength": 5000,
            "minLength": 1
          },
          "sound": {
            "description": "Whether sound is generated simultaneously when generating a video.",
            "type": "boolean",
            "default": true
          },
          "quality": {
            "default": "720p",
            "description": "The resolution of the generated video.",
            "enum": [
              "360p",
              "540p",
              "720p",
              "1080p"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "aspect_ratio": {
            "default": "16:9",
            "description": "The aspect ratio of the generated video.",
            "enum": [
              "16:9",
              "9:16",
              "1:1",
              "4:3",
              "3:4",
              "2:3",
              "3:2",
              "21:9"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "seed": {
            "description": "Random seed for reproducibility.",
            "type": "integer"
          },
          "images": {
            "description": "Image reference array (1–7 items).",
            "type": "array",
            "minItems": 1,
            "maxItems": 7,
            "items": {
              "type": "object",
              "required": [
                "image"
              ],
              "properties": {
                "image": {
                  "description": "First frame of the video; Notes: 1.Public URL or Base64. 2.Aspect ratios must be close: ratio between start/end frame must be in 0.8~1.25. 3.Format: png, jpeg, jpg, webp. 4.The post body of the HTTP request should not exceed 20MB",
                  "type": "string",
                  "x-ui-component": "uploaders"
                },
                "type": {
                  "description": "Reference type. For v5.5+: \"subject\" or \"background\".",
                  "type": "string",
                  "enum": [
                    "subject",
                    "background"
                  ],
                  "x-ui-component": "select"
                },
                "ref_name": {
                  "description": "Optional name for the reference image (v5.5+). Used to improve accuracy. Max 30 bytes (UTF-8).",
                  "type": "string",
                  "maxLength": 30
                }
              }
            }
          }
        },
        "required": [
          "model",
          "prompt",
          "images"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "images",
          "duration",
          "aspect_ratio",
          "quality",
          "sound",
          "seed"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created.",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "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.",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "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/result/{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"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "model": "pixverse/c1/reference-to-video",
  "description": "PixVerse C1 Reference-to-Video"
}

LLM-Friendly Prompt Template

# pixverse/c1/reference-to-video

> Pixverse c1 Reference-to-Video model. High-quality video generation from image prompts.


## 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**: `pixverse/c1/reference-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: `"pixverse/c1/reference-to-video"`

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`images`** (`array[object]`, _required_):
  Image reference array (1–7 items).
  - Min items: 1
  - Max items: 7
  - Item properties:
    - **`image`** (`string`, _required_):
      First frame of the video; Notes: 1.Public URL or Base64. 2.Aspect ratios must be close: ratio between start/end frame must be in 0.8~1.25. 3.Format: png, jpeg, jpg, webp. 4.The post body of the HTTP request should not exceed 20MB

    - **`type`** (`string`, _optional_):
      Reference type. For v5.5+: "subject" or "background".
      - Options: "subject", "background"

    - **`ref_name`** (`string`, _optional_):
      Optional name for the reference image (v5.5+). Used to improve accuracy. Max 30 bytes (UTF-8).


- **`duration`** (`integer`, _optional_):
  The duration of the generated media in seconds (1-15).
  - Default: `5`
  - Options: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

- **`aspect_ratio`** (`string`, _optional_):
  The aspect ratio of the generated video.
  - Default: `"16:9"`
  - Options: "16:9", "9:16", "1:1", "4:3", "3:4", "2:3", "3:2", "21:9"

- **`quality`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"720p"`
  - Options: "360p", "540p", "720p", "1080p"

- **`sound`** (`boolean`, _optional_):
  Whether sound is generated simultaneously when generating a video.
  - Default: `true`

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility.



**Required Parameters Example**:

```json
{
  "model": "pixverse/c1/reference-to-video",
  "prompt": "",
  "images": [
    {
      "image": "",
      "type": "subject",
      "ref_name": ""
    }
  ]
}
```


**Full Example**:

```json
{
  "model": "pixverse/c1/reference-to-video",
  "prompt": "",
  "images": [
    {
      "image": "",
      "type": "subject",
      "ref_name": ""
    }
  ],
  "duration": 5,
  "aspect_ratio": "16:9",
  "quality": "720p",
  "sound": true,
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`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.

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

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## 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": "pixverse/c1/reference-to-video",
  "prompt": "",
  "images": [
    {
      "image": "",
      "type": "subject",
      "ref_name": ""
    }
  ],
  "duration": 5,
  "aspect_ratio": "16:9",
  "quality": "720p",
  "sound": true,
  "seed": 0
}'

# 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/pixverse/c1/reference-to-video)

The mecha@image1 looks up at the sky from the ground@image2 .

PixVerse C1 Reference-to-Video

PixVerse C1 Reference-to-Video enables subject-consistent video generation by accepting up to 7 image references. Specify reference images as subjects or backgrounds, and the model generates a video that faithfully preserves the visual identity of your referenced elements.

Why Choose This?

Subject consistency Maintain character and object identity across the generated video.
Multiple references Use up to 7 image references for rich, multi-subject scenes.
Reference typing Tag each reference as a "subject" or "background" for precise control.
High resolution output Generate videos in 360p, 540p, 720p, or 1080p quality.
Flexible duration Create videos from 1 to 15 seconds in length.
Audio generation Optional synchronized audio that matches your scene.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the desired scene and motion
images	Yes	Array of image reference objects (1–7 items)
images[].image	Yes	Reference image (public URL or Base64, max 20MB)
images[].type	No	Reference type: "subject" or "background" (v5.5+)
images[].ref_name	No	Name for this reference, used in prompt mentions (max 30 bytes, v5.5+)
model	Yes	Model name (default: c1)
duration	No	Video length in seconds (1-15, default: 5)
quality	No	Output resolution: 360p, 540p, 720p (default), 1080p
aspect_ratio	No	Video aspect ratio (default: 16:9)
sound	No	Generate synchronized audio (default: enabled)
seed	No	Random seed for reproducibility

How to Use

Prepare your reference images — gather the subject and/or background images you want to use.
Write your prompt — describe the scene and motion; mention subjects by ref_name if assigned.
Build the images array — provide each image with its type and optional ref_name.
Set quality — choose resolution based on your quality and speed requirements.
Adjust duration — set video length up to 15 seconds.
Configure audio (optional) — enable or disable synchronized audio generation.
Run — submit and download your video.

Best Use Cases

Character Animation — Generate videos featuring specific people or characters with visual consistency.
Product Videos — Keep product appearance consistent throughout the generated video.
Brand Content — Maintain brand identity elements across video generations.
Multi-character Scenes — Include multiple distinct subjects in a single video.
Custom Backgrounds — Fix a specific environment as the video backdrop.

Pro Tips

Assign ref_name to each reference and mention them in your prompt for better accuracy.
Use type: "subject" for characters and objects, type: "background" for environments.
Use clean, high-quality images with clear subjects for the best consistency results.
Limit references to the most essential subjects — too many may reduce quality.
Ensure image URLs are publicly accessible; base64 is supported for private assets.

Pricing

Quality	Billing Standard	Without Audio	With Audio
360p	per second	$0.030	$0.040
540p	per second	$0.040	$0.050
720p	per second	$0.050	$0.065
1080p	per second	$0.095	$0.120

Notes

prompt and images are required fields.
Up to 7 image references supported for C1 and v5.5+ models.
ref_name max length is 30 bytes (UTF-8).
Supported image formats: PNG, JPEG, JPG, WebP.
Image aspect ratio should be between 1:2.5 and 2.5:1; minimum dimension 300px.
Maximum video duration is 15 seconds.

PixVerse C1 Text-to-Video — Generate video from text without reference images.
PixVerse C1 Image-to-Video — Animate a single reference image.

VEED Lipsync

VEED Lipsync re-drives the lip movements of an existing talking-head video to match a new audio track, preserving identity, appearance and background.

LIP-SYNC

From

$0.013/SEC