Browse ModelsWavespeed AIWan 2.2 T2V 720p LoRA

Wan 2.2 T2V 720p LoRA

Wan 2.2 T2V 720p LoRA

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Wan 2.2 T2V 720p with custom LoRA support turns text prompts into 720p AI videos and enables unlimited video generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.2 Text-to-Video 720p LoRA

Wan 2.2 Text-to-Video 720p LoRA is a powerful text-to-video generation model that creates stunning 720p HD videos from text descriptions. With advanced LoRA support including high-noise and low-noise options, apply custom styles, artistic effects, or consistent character appearances to create unique video content.


Why It Stands Out

  • HD 720p output: Generate crisp videos in landscape (1280×720) or portrait (720×1280) formats.
  • Advanced LoRA support: Three types of LoRA inputs for precise style control.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Negative prompt support: Exclude unwanted elements for cleaner outputs.
  • Flexible duration: Choose between 5 or 8 second video lengths.
  • 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.
sizeNoOutput resolution: 1280×720 or 720×1280 (default: 1280×720).
durationNoVideo length: 5 or 8 seconds (default: 5).
lorasNoStandard LoRA models to apply.
high_noise_lorasNoLoRAs applied during high-noise denoising steps.
low_noise_lorasNoLoRAs applied during low-noise denoising steps.
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. Select size — choose landscape (1280×720) or portrait (720×1280).
  4. Set duration — choose 5 or 8 seconds.
  5. Add LoRAs (optional) — apply standard, high-noise, or low-noise LoRAs.
  6. Click Run and wait for your video to generate.
  7. Preview and download the result.

Understanding LoRA Types

  • Standard LoRAs: Applied throughout the generation process.
  • High-Noise LoRAs: Applied during early denoising steps for structural/compositional effects.
  • Low-Noise LoRAs: Applied during later denoising steps for fine detail and style refinement.

Combining different LoRA types gives you precise control over the final output.


Best Use Cases

  • Creative Animation — Apply unique visual styles and effects.
  • Social Media Content — Create platform-optimized videos for TikTok, Reels, and Shorts.
  • Marketing & Advertising — Produce stylized promotional videos.
  • Artistic Projects — Generate videos with specific aesthetic styles.
  • Character Consistency — Maintain character appearance across multiple videos.

Pricing

DurationPrice
5 seconds$0.35
8 seconds$0.56

Pro Tips for Best Quality

  • Be detailed in your prompt — describe subject, action, environment, lighting, and mood.
  • Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
  • Experiment with different LoRA combinations for unique effects.
  • Use high-noise LoRAs for overall style, low-noise LoRAs for detail refinement.
  • Choose portrait (720×1280) for mobile-first platforms like TikTok.
  • Fix the seed when iterating to compare different LoRA combinations.

Notes

  • Ensure uploaded LoRA paths are correct and accessible.
  • 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-2.2/t2v-720p-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "size": "1280*720",
    "duration": 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.
sizestringNo1280*7201280*720, 720*1280The size of the generated media in pixels (width*height).
durationintegerNo55, 8The duration of the generated media in seconds.
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
high_noise_lorasarrayNo--List of high noise LoRAs to apply (max 3).
low_noise_lorasarrayNo--List of low noise LoRAs to apply (max 3).
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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