Browse ModelsAlibabaAlibaba Wan 2.7 Reference To Video

Alibaba Wan 2.7 Reference To Video

Alibaba Wan 2.7 Reference To Video

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Alibaba WAN 2.7 Reference-to-Video turns character, prop, or scene references from images or videos into new video shots with preserved identity, style, and layout plus smooth, coherent motion. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

Wan 2.7 Reference-to-Video

Wan 2.7 Reference-to-Video generates new video scenes guided by reference videos and an optional reference image, maintaining consistent characters, styles, and visual identity. Upload one or more reference videos, describe the scene you want, and the model produces a coherent, character-consistent video that brings your references into a new context.


Why Choose This?

  • Multi-video reference support Upload multiple reference videos to combine characters or visual elements from different sources into a single new scene.

  • Character-consistent generation The model preserves the identity, appearance, and style of characters from your reference videos throughout the generated clip.

  • Optional reference image Provide an additional still image to further guide the visual composition or introduce a new element.

  • Negative prompt support Specify what you don’t want in the output for more precise scene control.

  • Prompt expansion Enable enable_prompt_expansion to let the model automatically enrich and optimize your prompt before generation.

  • Resolution options Generate at 720p or 1080p to match your delivery requirements.


Parameters

ParameterRequiredDescription
videosYesOne or more reference videos. Click Add Item to include additional videos.
promptYesText description of the desired scene and action. Reference characters as “Video 1”, “Video 2” etc.
imageNoOptional reference image to supplement the video references.
negative_promptNoElements to exclude from the generated video.
resolutionNoOutput resolution: 720p (default) or 1080p.
aspect_ratioNoOutput aspect ratio. Default: 16:9.
durationNoClip length in seconds. Default: 5.
enable_prompt_expansionNoEnable automatic prompt optimization before generation. Default: off.
seedNoRandom seed for reproducible results. Use -1 for a random seed.

How to Use

  1. Upload your reference videos — provide one or more source videos via URL or drag-and-drop. Click Add Item to add more.
  2. Write your prompt — describe the new scene, referencing characters by position (e.g., “The characters in Video 1 and Video 2 are sitting in front of the TV and playing video games together.”).
  3. Upload reference image (optional) — provide a still image to supplement the visual references.
  4. Add negative prompt (optional) — specify elements you want to exclude from the output.
  5. Select resolution — 720p for standard output, 1080p for higher-quality results.
  6. Select aspect ratio — choose the format that fits your target platform.
  7. Set duration — choose your desired clip length in seconds.
  8. Enable prompt expansion (optional) — let the model automatically enrich your prompt before generation.
  9. Set seed (optional) — fix the seed to reproduce a specific result in future runs.
  10. Submit — generate, preview, and download your video.

Pricing

Duration720p1080p
5s$1.00$1.60
10s$1.50$2.40
15s$2.00$3.20

Billing Rules

  • 720p: base rate + fixed reference processing cost
  • 1080p: 1.6× the 720p cost
  • Pricing includes a fixed overhead for reference video processing in addition to the selected duration

Best Use Cases

  • Character-Driven Storytelling — Place characters from multiple reference videos into entirely new scenarios.
  • Fan Content & IP Crossovers — Combine characters from different sources into a single coherent scene.
  • Marketing & Brand Video — Generate new scenes featuring consistent brand characters or spokespeople from reference footage.
  • Creative Concepting — Rapidly prototype multi-character scenes for pitching and storyboarding.
  • Social Media Content — Create novel, character-consistent short-form video from existing footage.

Pro Tips

  • Use “Video 1”, “Video 2” etc. in your prompt to refer to specific reference videos in order.
  • The more distinct and clear each reference video is, the better the character consistency in the output.
  • Use negative_prompt to prevent unintended blending of visual styles between reference videos.
  • Enable prompt expansion for shorter or less detailed prompts to get richer output automatically.
  • Start with 720p to test your scene composition before committing to a 1080p final render.

Notes

  • Both videos and prompt are required fields; all other parameters are optional.
  • Ensure video and image URLs are publicly accessible if using links rather than direct uploads.
  • Please ensure your content complies with Alibaba’s usage policies.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/alibaba/wan-2.7/reference-to-video" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "resolution": "720p",
    "aspect_ratio": "16:9",
    "duration": 5,
    "enable_prompt_expansion": false,
    "seed": -1
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
imagestringNo-URL to a single reference image.
videosarrayYes-1 ~ 5 itemsArray of URLs to reference videos (max 4).
negative_promptstringNo-The negative prompt for the generation.
resolutionstringNo720p720p, 1080pThe resolution of the generated video.
aspect_ratiostringNo16:916:9, 9:16, 1:1, 4:3, 3:4The aspect ratio of the generated video.
durationintegerNo52 ~ 10The duration of the generated media in seconds (2-10s).
enable_prompt_expansionbooleanNofalse-If set to true, the prompt optimizer will be enabled.
seedintegerNo-1-1 ~ 2147483647The random seed to use for the generation. -1 means a random seed will be used.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsobjectArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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