Wan 2.1 T2V 480p

Wan 2.1 T2V 480p

Playground

Try it on WavespeedAI!

Wan 2.1 creates unlimited text-to-video content at 480P from simple text prompts, ideal for prototyping and content generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.1 Text-to-Video 480p

Generate dynamic videos from text descriptions with Wan 2.1 Text-to-Video 480p. This efficient model transforms your written prompts into smooth, visually appealing 480p videos — perfect for quick iterations, social content, and cost-effective video generation at scale.

Why It Stands Out

  • Pure text-to-video generation: No source image needed — simply describe your scene and watch it come to life.
  • Fast and affordable: Optimized for speed and cost efficiency while maintaining strong visual quality.
  • Prompt Enhancer: Built-in AI-powered prompt optimization helps you craft better descriptions for improved results.
  • Negative prompt support: Exclude unwanted elements for cleaner, more controlled outputs.
  • Flexible duration: Generate 5-second or 10-second clips depending on your needs.
  • Reproducibility: Use the seed parameter to recreate exact results or iterate on variations.

Pricing

DurationPrice
5 seconds$0.20
10 seconds$0.30

Parameters

ParameterRequiredDescription
promptYesText description of the video you want to generate.
negative_promptNoElements to avoid in the generated video.
sizeNoOutput resolution (default: 832×480).
num_inference_stepsNoQuality/speed trade-off (default: 30).
durationNoVideo length in seconds: 5 or 10 (default: 5).
guidance_scaleNoPrompt adherence strength (default: 5).
flow_shiftNoMotion intensity control (default: 3).
seedNoSet for reproducibility; -1 for random.

How to Use

  1. Write a prompt describing the scene, action, and style you want. Use the Prompt Enhancer for AI-assisted optimization.
  2. Set parameters — adjust size, duration, guidance scale, and other settings as needed.
  3. Add a negative prompt (optional) to exclude unwanted elements.
  4. Click Run and wait for your video to generate.
  5. Preview and download the result.

Best Use Cases

  • Social Media Content — Create quick, engaging video clips for TikTok, Reels, and Shorts.
  • Rapid Prototyping — Test video concepts quickly before committing to higher resolutions.
  • Marketing Previews — Generate draft videos for client approval at low cost.
  • Content at Scale — Produce high volumes of video content affordably.
  • Creative Exploration — Experiment with different prompts and styles without breaking the budget.

Pro Tips for Best Quality

  • Be specific in your prompt — describe subject, action, environment, lighting, and camera movement.
  • Use negative prompts to reduce common artifacts: blur, distortion, jitter, or watermarks.
  • Start with lower inference steps for quick previews, then increase for final renders.
  • Fix the seed when iterating to isolate the effect of parameter changes.
  • Use 480p for drafts and testing, then upgrade to 720p for final delivery if needed.

Notes

  • Processing time varies based on duration and current queue load.
  • Please ensure your prompts comply with content 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/t2v-480p" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "size": "832*480",
    "num_inference_steps": 30,
    "duration": 5,
    "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
promptstringYes-The positive prompt for the generation.
negative_promptstringNo-The negative prompt for the generation.
sizestringNo832*480832*480, 480*832The size of the generated media in pixels (width*height).
num_inference_stepsintegerNo301 ~ 40The number of inference steps to perform.
durationintegerNo55 ~ 10The duration of the generated media in seconds.
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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