Browse ModelsWavespeed AIWan 2.1 T2V 720p LoRA Ultra Fast

Wan 2.1 T2V 720p LoRA Ultra Fast

Wan 2.1 T2V 720p LoRA Ultra Fast

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WAN 2.1 Text-to-Video 720P delivers unlimited ultra-fast videos from text prompts and supports custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.1 Text-to-Video 720p LoRA Ultra Fast

Wan 2.1 Text-to-Video 720p LoRA Ultra Fast is a lightning-fast text-to-video generation model with full LoRA support. Generate HD 720p videos from text descriptions in seconds, with custom styles and effects — perfect for rapid iteration and high-volume content creation.


Why It Stands Out

  • Ultra-fast processing: Optimized for speed without sacrificing quality.
  • LoRA support: Apply custom LoRA models for specific styles and effects.
  • HD 720p output: Generate crisp 1280×720 videos with rich detail.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Negative prompt support: Exclude unwanted elements for cleaner outputs.
  • Fine-tuned control: Adjust guidance scale and flow shift for precise results.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
promptYesText description of the video you want to generate.
negative_promptNoElements to avoid in the output.
lorasNoLoRA models to apply (path and scale).
sizeNoOutput resolution: 1280×720 (default: 1280×720).
num_inference_stepsNoQuality/speed trade-off (default: 30).
durationNoVideo length: 5 or 10 seconds (default: 5).
guidance_scaleNoPrompt adherence strength (default: 5).
flow_shiftNoMotion flow control (default: 5).
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. Add a negative prompt (optional) — specify elements to exclude.
  3. Add LoRAs (optional) — select LoRA models and adjust their scale. Check recommended LoRAs for inspiration.
  4. Set duration — choose 5 or 10 seconds.
  5. Adjust parameters (optional) — fine-tune guidance scale and flow shift.
  6. Click Run and wait for your video to generate.
  7. Preview and download the result.

How to Use LoRA

LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.

  • Add LoRA: Enter the LoRA path and adjust the scale (0.0–1.0).
  • Recommended LoRAs: Check the interface for suggested LoRAs with preview images (e.g., Fire effects).
  • Scale adjustment: Higher scale means stronger style effect.

Best Use Cases

  • Rapid Prototyping — Quickly test video concepts with custom styles.
  • Visual Effects — Apply effects like fire, water, smoke with specialized LoRAs.
  • Social Media Content — Create stylized videos for TikTok, Reels, and Shorts.
  • Batch Processing — Generate multiple videos efficiently at scale.
  • Creative Exploration — Experiment with different LoRA combinations.

Pricing

DurationPrice
5 seconds$0.225
10 seconds$0.3375

Pro Tips for Best Quality

  • Be detailed in your prompt — describe subject, action, environment, lighting, and mood.
  • Use LoRAs to apply specific visual effects like fire, explosions, or weather.
  • Start with LoRA scale around 0.7–1.0 and adjust based on results.
  • Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
  • Check recommended LoRAs for proven style effects.
  • Fix the seed when iterating to compare different LoRA combinations.

Notes

  • Processing time is optimized for speed — expect quick turnaround.
  • Higher num_inference_steps produces better quality but increases generation time.
  • 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-720p-lora-ultra-fast" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "loras": [
        {
            "path": "Remade-AI/Fire",
            "scale": 1
        }
    ],
    "size": "1280*720",
    "num_inference_steps": 30,
    "duration": 5,
    "guidance_scale": 5,
    "flow_shift": 5,
    "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.
lorasarrayNomax 3 itemsList of LoRAs to apply (max 3).
loras[].pathstringYes-Path to the LoRA model
loras[].scalefloatYes-0.0 ~ 4.0Scale of the LoRA model
sizestringNo1280*7201280*720, 720*1280The 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_shiftnumberNo51.0 ~ 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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