Browse ModelsWavespeed AIWan 2.1 V2V 480p Ultra Fast

Wan 2.1 V2V 480p Ultra Fast

Wan 2.1 V2V 480p Ultra Fast

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Ultra-fast Wan 2.1 Video-to-Video (v2v) model for generating unlimited AI videos at 480p from existing video inputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.1 Video-to-Video 480p Ultra Fast

Wan 2.1 Video-to-Video 480p Ultra Fast is a lightning-fast video transformation model optimized for speed and efficiency. Convert existing videos into new styles and visual treatments in seconds — perfect for rapid iteration, previews, and high-volume processing.


Why It Stands Out

  • Ultra-fast processing: Optimized for speed without sacrificing quality.
  • Video-to-video transformation: Convert videos into different styles while preserving motion.
  • Prompt-guided transformation: Describe the visual style you want to achieve.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Negative prompt support: Exclude unwanted elements for cleaner outputs.
  • Fine-tuned control: Adjust strength, guidance scale, and flow shift for precise results.
  • Affordable pricing: Cost-effective option for prototyping and batch processing.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
videoYesSource video to transform (upload or public URL).
promptYesText description of the desired visual style.
negative_promptNoElements to avoid in the output.
num_inference_stepsNoQuality/speed trade-off (default: 30).
durationNoOutput video length: 5 or 10 seconds (default: 5).
strengthNoTransformation intensity (0.0–1.0, default: 0.9).
guidance_scaleNoPrompt adherence strength (default: 5).
flow_shiftNoMotion flow control (default: 3).
seedNoSet for reproducibility; -1 for random.

How to Use

  1. Upload your source video — drag and drop a file or paste a public URL.
  2. Write a prompt describing the visual style you want. Use the Prompt Enhancer for AI-assisted optimization.
  3. Add a negative prompt (optional) — specify elements to exclude.
  4. Adjust parameters — set strength, guidance scale, and other settings as needed.
  5. Click Run and wait for your video to generate.
  6. Preview and download the result.

Best Use Cases

  • Rapid Prototyping — Quickly test style transformations before committing to higher resolutions.
  • Batch Processing — Transform multiple videos affordably at scale.
  • Style Exploration — Experiment with different visual treatments efficiently.
  • Content Previews — Generate quick previews for client approval.
  • Social Media Content — Create stylized videos for platforms where 480p is sufficient.

Pricing

DurationPrice
5 seconds$0.125
10 seconds$0.1875

Pro Tips for Best Quality

  • Use lower strength (0.5–0.7) to preserve more of the original video.
  • Use higher strength (0.8–0.95) for more dramatic style transformations.
  • Use negative prompts to reduce artifacts like blur, distortion, or unwanted elements.
  • Start with 480p Ultra Fast for testing, then upgrade to 720p for final delivery.
  • Fix the seed when iterating to compare different parameter settings.

Notes

  • Ensure uploaded video URLs are publicly accessible.
  • Processing time is optimized for speed — expect quick turnaround.
  • For higher resolution output, consider Wan 2.1 V2V 720p.
  • Please ensure your content complies with usage guidelines.

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/wavespeed-ai/wan-2.1/v2v-480p-ultra-fast" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "num_inference_steps": 30,
    "duration": 5,
    "strength": 0.9,
    "guidance_scale": 5,
    "flow_shift": 3,
    "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
videostringYes-The video for generating the output.
promptstringYes-
negative_promptstringNo-The negative prompt for the generation.
num_inference_stepsintegerNo301 ~ 40The number of inference steps to perform.
durationintegerNo55 ~ 10The duration of the generated media in seconds.
strengthnumberNo0.90.10 ~ 1.00
guidance_scalenumberNo50.00 ~ 20.00The guidance scale to use for the generation.
flow_shiftnumberNo31.0 ~ 10.0The shift value for the timestep schedule for flow matching.
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.outputsstringArray 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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